Awesome

Awesome-Mixup

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Welcome to Awesome-Mixup, a carefully curated survey of Mixup algorithms implemented in the PyTorch library, aiming to meet various needs of the research community. Mixup is a kind of methods that focus on alleviating model overfitting and poor generalization. As a "data-centric" way, Mixup can be applied to various training paradigms and data modalities.

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Introduction

We summarize awesome mixup data augmentation methods for visual representation learning in various scenarios from 2018 to 2024.

The list of awesome mixup augmentation methods is summarized in chronological order and is on updating. The main branch is modified according to Awesome-Mixup in OpenMixup and Awesome-Mix, and we are working on a comperhensive survey on mixup augmentations. You can read our survey: A Survey on Mixup Augmentations and Beyond see more detailed information.

To find related papers and their relationships, check out Connected Papers, which visualizes the academic field in a graph representation.
To export BibTeX citations of papers, check out ArXiv or Semantic Scholar of the paper for professional reference formats.

Figuer of Contents

You can see the figuer of mixup augmentation methods deirtly that we summarized.

Awesome-Mixup

Sample Mixup Policies in SL

Static Linear

mixup: Beyond Empirical Risk Minimization Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz ICLR'2018 [Paper] [Code]
<details close> <summary>MixUp Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204561478-80b77110-21a4-480f-b369-d2f0656b5382.png" /> </details>
Between-class Learning for Image Classification Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada CVPR'2018 [Paper] [Code]
<details close> <summary>BC Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204563476-15e638ad-6c25-4ab6-9bb6-4c5be74c625f.png" /> </details>
Preventing Manifold Intrusion with Locality: Local Mixup Raphael Baena, Lucas Drumetz, Vincent Gripon EUSIPCO'2022 [Paper]
<details close> <summary>LocalMixup Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/ce8cd7a2-486c-4f6d-98d3-358c1ba42285" /> </details>
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan ICLR'2020 [Paper] [Code]
<details close> <summary>AugMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204567137-f71b0437-9267-4f99-b7dc-911ffa4f8b73.png" /> </details>
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness Ryuichiro Hataya, Hideki Nakayama arXiv'2021 [Paper]
<details close> <summary>DJMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204567516-3d058e8c-4232-4af4-b7a8-efa3a1298a1b.png" /> </details>
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt CVPR'2022 [Paper] [Code]
<details close> <summary>PixMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204567828-b434c118-0be1-475d-a0f3-9834e39b4507.png" /> </details>
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers Zhenglin Huang, Xiaoan Bao, Na Zhang, Qingqi Zhang, Xiaomei Tu, Biao Wu, Xi Yang NIPS'2023 [Paper] [Code]
<details close> <summary>IPMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/55c1ce2d-5372-4821-a123-74723df7fc8d" /> </details>

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Feature-based

Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio ICML'2019 [Paper] [Code]
<details close> <summary>ManifoldMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204565193-c5416185-ed98-4b86-bc7c-f5b6cc2f839b.png" /> </details>
PatchUp: A Regularization Technique for Convolutional Neural Networks Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar arXiv'2020 [Paper] [Code]
<details close> <summary>PatchUp Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204566319-f2c67e8a-c10b-4ede-a57f-8c6ddf85e013.png" /> </details>
On Feature Normalization and Data Augmentation Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger CVPR'2021 [Paper] [Code]
<details close> <summary>MoEx Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204569196-3641255a-56f3-407a-986a-209b5c7eeff6.png" /> </details>
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN Minsoo Kang, Minkoo Kang, Suhyun Kim AAAI'2024 [Paper]
<details close> <summary>Catch-Up-Mix Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/Awesome-Mixup/assets/44519745/459a5d2b-cc74-4bd0-8cd2-46313652a9b8" /> </details>

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Cutting-based

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo ICCV'2019 [Paper] [Code]
<details close> <summary>CutMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204564166-49707535-43f9-4d15-af89-d1a5a302db24.png" /> </details>
Improved Mixed-Example Data Augmentation Cecilia Summers, Michael J. Dinneen WACV'2019 [Paper] [Code]
<details close> <summary>MixedExamples Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/232355479-8a53714f-5a6f-4c8e-b808-cc73f6d0f669.png" /> </details>
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu arXiv'2019 [Paper]
<details close> <summary>Pani VAT Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204572993-8b3fa627-8c36-4763-a2a6-c9a90c5f0fc2.png" /> </details>
FMix: Enhancing Mixed Sample Data Augmentation Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare arXiv'2020 [Paper] [Code]
<details close> <summary>FMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204564909-8d20a405-141d-4fe6-ae72-581fc24222f8.png" /> </details>
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers Jin-Ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee CVPRW'2020 [Paper] [Code]
<details close> <summary>SmoothMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204565814-fd528402-2a57-482b-b608-1ee3096984b0.png" /> </details>
GridMix: Strong regularization through local context mapping Kyungjune Baek, Duhyeon Bang, Hyunjung Shim Pattern Recognition'2021 [Paper] [Code]
<details close> <summary>GridMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204566566-b345e409-da4a-4f3b-b1bd-43e7c767d26a.png" /> </details>
ResizeMix: Mixing Data with Preserved Object Information and True Labels Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang arXiv'2020 [Paper] [Code]
<details close> <summary>ResizeMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204566840-69782b04-4645-41b3-a6eb-428977c63881.png" /> </details>
StackMix: A complementary Mix algorithm John Chen, Samarth Sinha, Anastasios Kyrillidis UAI'2022 [Paper]
<details close> <summary>StackMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204574552-fb5e9865-86b0-4d51-977c-82a840d81a18.png" /> </details>
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika, Mahmoud Melkemi arXiv'2022 [Paper] [Code]
<details close> <summary>SuperpixelGridCut Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204570617-090cc5a7-508c-49e7-9e5f-df7ec018f540.png" /> </details>
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective Chanwoo Park, Sangdoo Yun, Sanghyuk Chun NIPS'2022 [Paper] [Code]
<details close> <summary>MSDA Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204571363-f0b1a960-54f9-4462-855d-59e90f284cfe.png" /> </details>
You Only Cut Once: Boosting Data Augmentation with a Single Cut Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li ICML'2022 [Paper] [Code]
<details close> <summary>YOCO Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/c7040d23-6dd5-4634-9022-d26529810307" /> </details>
StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification Xin Jin, Hongyu Zhu, Mounîm A.El Yacoubi, Hongchao Liao, Huafeng Qin, Yun Jiang arXiv'2024 [Paper]
<details close> <summary>StarMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/44dbc86b-f7cd-4836-a93e-835f78d21ec8" /> </details>

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K Samples Mixup

You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi CVPR'2016 [Paper] [Code]
<details close> <summary>Mosaic</summary> <img width="50%" src="https://github.com/user-attachments/assets/11824bd1-befc-4edd-8d0d-d8199b123e4b" /> </details>
Data Augmentation using Random Image Cropping and Patching for Deep CNNs Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara IEEE TCSVT'2020 [Paper]
<details close> <summary>RICAP</summary> <img width="50%" src="https://github.com/user-attachments/assets/d8fae235-ae35-4fbe-9454-6a8f3b8fce12" /> </details>
k-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien arXiv'2021 [Paper]
<details close> <summary>k-Mixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204569488-d4862400-3304-488d-ad24-06eff4e0c0b2.png" /> </details>
Observations on K-image Expansion of Image-Mixing Augmentation for Classification Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi IEEE Access'2021 [Paper] [Code]
<details close> <summary>DCutMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/230492924-e9008de1-913f-44f5-ac2e-3d21f07d1b7f.png" /> </details>
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks Alexandre Rame, Remy Sun, Matthieu Cord ICCV'2021 [Paper]
<details close> <summary>MixMo Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/37af64e6-ab18-49fe-84a6-5a5104a3f81a" /> </details>
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu ACM MM;2021 [Paper]
<details close> <summary>Cut-Thumbnail</summary> <img width="50%" src="https://github.com/user-attachments/assets/450da799-0fdd-47e7-bcc4-5fcc5f186462" /> </details>

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Random Policies

RandomMix: A mixed sample data augmentation method with multiple mixed modes Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie arXiv'2022 [Paper]
<details close> <summary>RandomMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204570320-2785207c-aced-4d94-a33e-98acfcbbaa3f.png" /> </details>
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie ICME'2022 [Paper]
<details close> <summary>AugRmixAT Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204571167-748828be-a1f6-46ac-bc13-37cccfa72515.png" /> </details>

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Style-based

StyleMix: Separating Content and Style for Enhanced Data Augmentation Minui Hong, Jinwoo Choi, Gunhee Kim CVPR'2021 [Paper] [Code]
<details close> <summary>StyleMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204567999-6d0263c2-111c-4335-92b5-66486bb0fea0.png" /> </details>
Domain Generalization with MixStyle Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang ICLR'2021 [Paper] [Code]
<details close> <summary>MixStyle Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204568994-eb45528e-e83b-4ac5-bed9-642da987ec89.png" /> </details>
AlignMix: Improving representation by interpolating aligned features Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis CVPR'2022 [Paper] [Code]
<details close> <summary>AlignMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204574666-fcb694b0-c6bf-438a-bbd0-020635fe4b51.png" /> </details>

Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis NIPS'2023 [Paper] <details close> <summary>MultiMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/224786198-a85b76b9-0f8b-434d-be59-bbc41438aac9.png" /> </details>

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Saliency-based

SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae ICLR'2021 [Paper] [Code]
<details close> <summary>SaliencyMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204571915-624d3e5e-7678-4ba3-a08c-09ca2741bf72.png" /> </details>
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, Marios Savvides ICASSP'2020 [Paper] [Code]
<details close> <summary>AttentiveMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204572493-a8c816c9-2be5-43b6-bf35-580cfab8716f.png" /> </details>
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data Shaoli Huang, Xinchao Wang, Dacheng Tao AAAI'2021 [Paper] [Code]
<details close> <summary>SnapMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204572296-2035c6b4-c477-4484-a8e6-7ad9ad415045.png" /> </details>
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition Hao Li, Xiaopeng Zhang, Hongkai Xiong, Qi Tian VCIP'2020 [Paper]
<details close> <summary>AttributeMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204573220-13a3b90e-73f8-4277-a997-67dddb15dd1c.png" /> </details>
Where to Cut and Paste: Data Regularization with Selective Features Jiyeon Kim, Ik-Hee Shin, Jong-Ryul, Lee, Yong-Ju Lee ICTC'2020 [Paper] [Code]
<details close> <summary>FocusMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204567137-f71b0437-9267-4f99-b7dc-911ffa4f8b73.png" /> </details>
PuzzleMix: Exploiting Saliency and Local Statistics for Optimal Mixup Jang-Hyun Kim, Wonho Choo, Hyun Oh Song ICML'2020 [Paper] [Code]
<details close> <summary>PuzzleMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204573527-75f28e86-9b0e-4b14-bd21-ef89de52dd5f.png" /> </details>
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song ICLR'2021 [Paper] [Code]
<details close> <summary>Co-Mixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204573653-68ce31e8-fa01-4cf8-9493-c2311fd99e13.png" /> </details>
SuperMix: Supervising the Mixing Data Augmentation Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi CVPR'2021 [Paper] [Code]
<details close> <summary>SuperMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204573912-47ae05f3-8d78-4ef7-b3a9-15d54bffa20a.png" /> </details>
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li ECCV'2022 [Paper] [Code]
<details close> <summary>AutoMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/174272662-19ce57ad-7b08-4e73-81b1-3bb81fee2fe5.png" /> </details>
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li arXiv'2021 [Paper] [Code]
<details close> <summary>SAMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/174272657-fb662377-b7c3-4faa-8d9b-ea6f1e08549e.png" /> </details>
RecursiveMix: Mixed Learning with History Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang NIPS'2022 [Paper] [Code]
<details close> <summary>RecursiveMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204576092-5fd92410-c12a-4691-8f7b-01901445f2a4.png" /> </details>
TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation Tsz-Him Cheung, Dit-Yan Yeung OpenReview'2023 [Paper]
<details close> <summary>TransformMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204575385-0b2d7470-0ffd-4b6b-977b-ef28b1617954.png" /> </details>
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps Minsoo Kang, Suhyun Kim AAAI'2023 [Paper] [Code]
<details close> <summary>GuidedMixup Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/250170540-97434afd-790c-4148-81dc-ff7129ca3f7c.png" /> </details>
GradSalMix: Gradient Saliency-Based Mix for Image Data Augmentation Tao Hong, Ya Wang, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Jinwen Ma ICME'2023 [Paper]
<details close> <summary>GradSalMix Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/264142659-b8e2eef2-f6ea-4b03-bc03-0d2c08212f3a.png" /> </details>
LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition Fadi Dornaika, Danyang Sun TIP'2023 [Paper] [Code]
<details close> <summary>LGCOAMix Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/Awesome-Mixup/assets/44519745/802bdd05-dd5d-4d84-a66a-18d0fe9f2fa9" /> </details>
Adversarial AutoMixup Huafeng Qin, Xin Jin, Yun Jiang, Mounim A. El-Yacoubi, Xinbo Gao ICLR'2024 [Paper] [Code]
<details close> <summary>AdAutoMix Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/openmixup/assets/44519745/cdf24e60-e262-4d69-88a6-d1544e625679" /> </details>

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Attention-based

TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu ECCV'2022 [Paper] [Code]
<details close> <summary>TokenMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578736-7b2dd349-7214-4d49-ade8-30b1caa2f1ea.png" /> </details>
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim NIPS'2022 [Paper] [Code]
<details close> <summary>TokenMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578884-b9d7d466-b26b-4e4b-8a23-22199a6dca26.png" /> </details>
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran WACV'2023 [Paper]
<details close> <summary>ScoreMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204576297-e97ea9c4-ee17-4ec3-a672-3f088ededb72.png" /> </details>
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu ICLR'2023 [Paper] [Code]
<details close> <summary>MixPro Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/224795935-afb936b2-fc77-4018-a681-72887f96fa59.png" /> </details>
SMMix: Self-Motivated Image Mixing for Vision Transformers Mengzhao Chen, Mingbao Lin, ZhiHang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji ICCV'2023 [Paper] [Code]
<details close> <summary>SMMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/213537624-7359689e-b5af-4db1-a4ad-07876dd44089.png" /> </details>

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Generating Samples

Data Augmentation via Latent Space Interpolation for Image Classification *Xiaofeng Liu, Yang Zou, Lingsheng Kong, Zhihui Diao, Junliang Yan, Jun Wang, Site Li, Ping Jia, Jane You ICPR'2018 [Paper]
<details close> <summary>AEE Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/378e9d22-3031-4874-9dd0-09544c2c8324" /> </details>
On Adversarial Mixup Resynthesis Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal NIPS'2019 [Paper] [Code]
<details close> <summary>AMR Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/232315897-2a5fb2c5-d0ce-4c01-b6cd-21ec54bd9e49.png" /> </details>
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning Jianchao Zhu, Liangliang Shi, Junchi Yan, Hongyuan Zha ECCV'2020 [Paper]
<details close> <summary>AutoMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204572771-e09246ca-b88b-4755-8d8a-f99053244610.png" /> </details>
VarMixup: Exploiting the Latent Space for Robust Training and Inference Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian CVPRW'2021 [Paper]
<details close> <summary>VarMixup Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/050d0000-16c9-4bf5-afd7-cb23fe4d6cd0" /> </details>
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar CVPR'2024 [Paper] [Code]
<details close> <summary>DiffuseMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/8478e528-a96f-45af-81dc-a3469a6f84bd" /> </details>

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Label Mixup Policies in SL

Optimizing Calibration

Combining Ensembles and Data Augmentation can Harm your Calibration Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran ICLR'2021 [Paper] [Code]
<details close> <summary>CAMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204577092-06b2c74a-47cc-44f5-8423-9f37b1d0cbdc.png" /> </details>
RankMixup: Ranking-Based Mixup Training for Network Calibration Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham ICCV'2023 [Paper] [Code]
<details close> <summary>RankMixup Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/264144742-051304d2-4f64-4bd7-9e70-12074c2215e4.png" /> </details>
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, Jinwoo Shin NIPS'2021 [Paper] [Code]
<details close> <summary>SmoothMixup Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/ea280849-dda3-465c-959b-31d10a0cf54c" /> </details>

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Area-based

TransMix: Attend to Mix for Vision Transformers Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai CVPR'2022 [Paper] [Code]
<details close> <summary>TransMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204577728-8d59ad5f-0204-4943-aae7-dca6c48022ce.png" /> </details>
Data Augmentation using Random Image Cropping and Patching for Deep CNNs Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara IEEE TCSVT'2020 [Paper]
<details close> <summary>RICAP</summary> <img width="50%" src="https://github.com/user-attachments/assets/d8fae235-ae35-4fbe-9454-6a8f3b8fce12" /> </details>
RecursiveMix: Mixed Learning with History Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang NIPS'2022 [Paper] [Code]
<details close> <summary>RecursiveMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204576092-5fd92410-c12a-4691-8f7b-01901445f2a4.png" /> </details>

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Loss Object

Harnessing Hard Mixed Samples with Decoupled Regularizer Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li NIPS'2023 [Paper] [Code]
<details close> <summary>DecoupledMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578387-4be9567c-963a-4d2d-8c1f-c7c5ade527b8.png" /> </details>
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi UAI'2023 [Paper] [Code]
<details close> <summary>MixupE Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/209991074-3dd41cdf-4e64-42e2-8bf4-ebc60e8212d0.png" /> </details>

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Random Label Policies

Mixup Without Hesitation Hao Yu, Huanyu Wang, Jianxin Wu ICIG'2022 [Paper] [Code]
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania NIPS'2022 [Paper] [Code]
<details close> <summary>RegMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204571630-e8407bd7-ca27-44de-baca-5d88ca2004a6.png" /> </details>

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Optimizing Mixing Ratio

MixUp as Locally Linear Out-Of-Manifold Regularization Hongyu Guo, Yongyi Mao, Richong Zhang AAAI'2019 [Paper]
<details close> <summary>AdaMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204563766-4a49b4d9-fb1e-46d7-8443-bff37a527ee1.png" /> </details>
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania NIPS'2022 [Paper] [Code]
<details close> <summary>RegMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204571630-e8407bd7-ca27-44de-baca-5d88ca2004a6.png" /> </details>
Metamixup: Learning adaptive interpolation policy of mixup with metalearning Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen IEEE TNNLS'2021 [Paper]
<details close> <summary>MetaMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204576802-4aa83a66-61ac-40fd-8904-3b4f9eda62ef.png" /> </details>
LUMix: Improving Mixup by Better Modelling Label Uncertainty Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille, Philip Torr, Song Bai ICASSP'2024 [Paper] [Code]
<details close> <summary>LUMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/205531445-dc4b7790-e0b7-4c41-b9d2-708efa5e4198.png" /> </details>
SUMix: Mixup with Semantic and Uncertain Information Huafeng Qin, Xin Jin, Hongyu Zhu, Hongchao Liao, Mounîm A. El-Yacoubi, Xinbo Gao ECCV'2024 [Paper] [Code]
<details close> <summary>SUMix Framework</summary> <img width="50%" src="https://github.com/JinXins/SUMix/assets/124172716/1725dfb7-ab1e-4429-a34b-0dfdd3bc2a6f" /> </details>

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Generating Label

GenLabel: Mixup Relabeling using Generative Models Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee ICML'2022 [Paper] <details close> <summary>GenLabel Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578083-3212ed98-6f1b-422b-8764-0276a65bce8e.png" /> </details>

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Attention Score

All Tokens Matter: Token Labeling for Training Better Vision Transformers Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng NIPS'2021 [Paper] [Code]
<details close> <summary>Token Labeling Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204577372-f679ab10-a65f-4319-9a40-8393c20ad0fa.png" /> </details>
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu ECCV'2022 [Paper] [Code]
<details close> <summary>TokenMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578736-7b2dd349-7214-4d49-ade8-30b1caa2f1ea.png" /> </details>
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim NIPS'2022 [Paper] [Code]
<details close> <summary>TokenMixup Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578884-b9d7d466-b26b-4e4b-8a23-22199a6dca26.png" /> </details>
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu ICLR'2023 [Paper] [Code]
<details close> <summary>MixPro Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/224795935-afb936b2-fc77-4018-a681-72887f96fa59.png" /> </details>
Token-Label Alignment for Vision Transformers Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu ICCV'2023 [Paper] [Code]
<details close> <summary>TL-Align Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204579080-3b7c9352-8fb3-49bd-99f5-ce4f72d722d8.png" /> </details>

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Saliency Token

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data Shaoli Huang, Xinchao Wang, Dacheng Tao AAAI'2021 [Paper] [Code]
<details close> <summary>SnapMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204572296-2035c6b4-c477-4484-a8e6-7ad9ad415045.png" /> </details>
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang AAAI'2022 [Paper]
<details close> <summary>Saliency Grafting Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204577555-7ffe34ed-74c1-4dff-95e8-f5a9e385f50c.png" /> </details>

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Self-Supervised Learning

Contrastive Learning

MixCo: Mix-up Contrastive Learning for Visual Representation Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun NIPSW'2020 [Paper] [Code]
<details close> <summary>MixCo Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204580767-c0730ac6-802f-40bc-92a8-4b7abe0acb99.png" /> </details>
Hard Negative Mixing for Contrastive Learning Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus NIPS'2020 [Paper] [Code]
<details close> <summary>MoCHi Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204580935-80ebaaaa-2761-4da4-9b58-7755a0dc15c6.png" /> </details>
i-Mix A Domain-Agnostic Strategy for Contrastive Representation Learning Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee ICLR'2021 [Paper] [Code]
<details close> <summary>i-Mix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204581084-5460bd37-4adb-4f01-b7af-fc88ceb2683e.png" /> </details>
Beyond Single Instance Multi-view Unsupervised Representation Learning Xiangxiang Chu, Xiaohang Zhan, Xiaolin Wei BMVC'2022 [Paper]
<details close> <summary>BSIM Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204581834-3ead412b-359c-40ba-86ea-3ab54ead2c96.png" /> </details>
Improving Contrastive Learning by Visualizing Feature Transformation Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen ICCV'2021 [Paper] [Code]
<details close> <summary>FT Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204582514-426efca3-4e8b-48b8-b69a-ecebb94b7fa8.png" /> </details>
Mix-up Self-Supervised Learning for Contrast-agnostic Applications Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann ICME'2021 [Paper]
<details close> <summary>MixSSL Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204583625-58632669-05f0-445b-bd41-60aa37b515d4.png" /> </details>
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng NIPS'2021 [Paper] [Code]
<details close> <summary>Co-Tuning Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/4f43b519-4781-4e4b-b57f-47ae96af7d80" /> </details>
Center-wise Local Image Mixture For Contrastive Representation Learning Hao Li, Xiaopeng Zhang, Hongkai Xiong BMVC'2021 [Paper]
<details close> <summary>CLIM Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204583930-15ab3916-9d8b-4adf-9a79-e40eabbbc255.png" /> </details>
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng OpenReview'2021 [Paper]
<details close> <summary>PCEA Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204582781-b54c4472-67eb-4e78-9362-44b990bbafa3.png" /> </details>
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li arXiv'2021 [Paper] [Code]
<details close> <summary>SAMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/174272657-fb662377-b7c3-4faa-8d9b-ea6f1e08549e.png" /> </details>
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du OpenReview'2021 [Paper]
<details close> <summary>MixSiam Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204583448-8c1890fd-ce95-488a-9570-f7393a4d140a.png" /> </details>
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das NIPS'2021 [Paper] [Code]
<details close> <summary>CoMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204583146-fc363695-6889-46cd-93f1-236bec9d5fb5.png" /> </details>
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing AAAI'2022 [Paper] [Code]
<details close> <summary>Un-Mix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204581434-dfbc11f8-e300-4dd7-bc84-adbe5a53dbf4.png" /> </details>
m-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang KDD'2022 [Paper] [Code]
<details close> <summary>m-Mix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204582292-eef1f644-f39f-46ae-98ba-313501bdb515.png" /> </details>
A Simple Data Mixing Prior for Improving Self-Supervised Learning Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie CVPR'2022 [Paper] [Code]
<details close> <summary>SDMP Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204585207-6fee3174-224d-44e6-bbd1-514d6697d128.png" /> </details>
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping Junlin Han, Lars Petersson, Hongdong Li, Ian Reid arXiv'2022 [Paper] [Code]
<details close> <summary>CropMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204585732-680295fe-4768-4199-af72-bda10edda644.png" /> </details>
Mixing up contrastive learning: Self-supervised representation learning for time series Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen PR Letter'2022 [Paper]
<details close> <summary>MCL Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/f3a7dc05-e125-4ac4-97c4-81b644e1e97c" /> </details>
Towards Domain-Agnostic Contrastive Learning Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le ICML'2021 [Paper]
<details close> <summary>DACL Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/MogaNet/assets/44519745/19c8e3cb-db6f-463f-a765-c243b2f9e45a" /> </details>
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li ICML'2022 [Paper] [Code]
<details close> <summary>ProGCL Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204584450-e31a6c9f-0f1c-4342-b907-1b80cae547ab.png" /> </details>
Evolving Image Compositions for Feature Representation Learning Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez BMVC'2021 [Paper]
<details close> <summary>PatchMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204574267-8e53783d-ea54-4399-8c32-86c8ac2520bd.png" /> </details>
On the Importance of Asymmetry for Siamese Representation Learning Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen CVPR'2022 [Paper] [Code]
<details close> <summary>ScaleMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204585304-03b6aa42-205c-4650-9d90-ecfc0928734e.png" /> </details>
Geodesic Multi-Modal Mixup for Robust Fine-Tuning Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song NIPS'2023 [Paper] [Code]
<details close> <summary>m2-Mix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/ad6ca047-f04b-463b-9954-eaa15336d4ce" /> </details>

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Masked Image Modeling

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable Kevin Zhang, Zhiqiang Shen arXiv'2022 [Paper] [Code]
<details close> <summary>i-MAE Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/211220785-5031f97c-c9a3-4ade-b344-48db01fc3760.png" /> </details>
MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li CVPR'2023 [Paper] [Code]
<details close> <summary>MixMAE Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204315480-5c59ed60-7b5f-4da9-85fb-551a961fd731.png" /> </details>
Mixed Autoencoder for Self-supervised Visual Representation Learning Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung CVPR'2023 [Paper]
<details close> <summary>MixedAE Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/229929023-1ea53237-ebfb-4203-8b93-dd761d937b27.png" /> </details>

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Semi-Supervised Learning

MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel NIPS'2019 [Paper] [Code]
<details close> <summary>MixMatch Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204580441-1fd71bf7-63f3-4935-9332-287642e0bcc8.png" /> </details>
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel ICLR'2020 [Paper] [Code]
<details close> <summary>ReMixMatch Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204579631-529bb505-a858-441f-9030-4a9b44273330.png" /> </details>
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Junnan Li, Richard Socher, Steven C.H. Hoi ICLR'2020 [Paper] [Code]
<details close> <summary>DivideMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/230495626-f0f3f52e-9f8a-472d-8ff2-b33356993e09.png" /> </details>
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning Tong Wei, Feng Shi, Hai Wang, Wei-Wei Tu. Yu-Feng Li arXiv'2020 [Paper]
<details close> <summary>MixPUL Framework</summary>

</details>

Milking CowMask for Semi-Supervised Image Classification Geoff French, Avital Oliver, Tim Salimans NIPS'2020 [Paper] [Code]
<details close> <summary>CowMask Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/aa05ab9c-107e-4c3f-bec0-d93ddcd54bb1" /> </details>
Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff Vincent Pisztora, Yanglan Ou, Xiaolei Huang, Francesca Chiaromonte, Jia Li arXiv'2021 [Paper]
<details close> <summary>Epsilon Consistent Mixup (ϵmu) Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/283528510-1f3b643c-0edd-416e-9979-110f3d2be6b6.png" /> </details>
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning Changchun Li, Ximing Li, Lei Feng, Jihong Ouyang ICLR'2021 [Paper]
<details close> <summary>P3Mix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/472870f9-e6f8-4b63-8c05-040db0e255ef" /> </details>
Interpolation Consistency Training for Semi-Supervised Learning Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz NN'2022 [Paper]
<details close> <summary>ICT Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/912458fa-9631-4746-8ba4-8c57ecad476b" /> </details>
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Tao Tan, Xinlin Zhang, Tong Tong arXiv'2023 [Paper]
<details close> <summary>DCPA Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/268410560-a45c03d9-beb1-4b74-a34b-4d1ecd356de9.png" /> </details>
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak CVPR'2022 [Paper] [Code]
<details close> <summary>MUM Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/225082975-4143e7f5-8873-433c-ab6f-6caa615f7120.png" /> </details>
Harnessing Hard Mixed Samples with Decoupled Regularizer Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li NIPS'2023 [Paper] [Code]
<details close> <summary>DFixMatch Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204578387-4be9567c-963a-4d2d-8c1f-c7c5ade527b8.png" /> </details>
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi arXiv'2023 [Paper] [Code]
<details close> <summary>MixEMatch Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/268411562-4263ccd5-a31c-4020-9281-ba4bc3d9fc54.png" /> </details>
LaserMix for Semi-Supervised LiDAR Semantic Segmentation Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu CVPR'2023 [Paper] [Code] [project]
<details close> <summary>LaserMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/209255964-69cab84b-ae54-4e74-be4f-a23a836c665c.png" /> </details>
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation Yu Lei, Haolun Luo, Lituan Wang, Zhenwei Zhang, Lei Zhang arXiv'2024 [Paper] [Code]
<details close> <summary>PCLMix Framework</summary> <img width="50%" src="https://github.com/Lupin1998/Awesome-MIM/assets/44519745/e41b7e65-962b-49fa-b1be-6e789e8b244e" /> </details>

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CV Downstream Tasks

Regression

RegMix: Data Mixing Augmentation for Regression Seong-Hyeon Hwang, Steven Euijong Whang arXiv'2021 [Paper]
<details close> <summary>MixRL Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/6849a6a4-a6cf-4bfc-b483-dd84589de6dc" /> </details>
C-Mixup: Improving Generalization in Regression Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou, Chelsea Finn NIPS'2022 [Paper] [Code]
<details close> <summary>C-Mixup Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/721e55da-cdda-4b22-9f72-968d954c6485" /> </details>
ExtraMix: Extrapolatable Data Augmentation for Regression using Generative Models Kisoo Kwon, Kuhwan Jeong, Sanghyun Park, Sangha Park, Hoshik Lee, Seung-Yeon Kwak, Sungmin Kim, Kyunghyun Cho OpenReview'2022 [Paper]
<details close> <summary>SupReMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/72a6c717-ab7c-480a-b840-c7b789ae4277" /> </details>
Rank-N-Contrast: Learning Continuous Representations for Regression Kaiwen Zha, Peng Cao, Jeany Son, Yuzhe Yang, Dina Katabi NIPS'2023 [Paper] [Code]
Anchor Data Augmentation Nora Schneider, Shirin Goshtasbpour, Fernando Perez-Cruz NIPS'2023 [Paper]
Mixup Your Own Pairs Yilei Wu, Zijian Dong, Chongyao Chen, Wangchunshu Zhou, Juan Helen Zhou arXiv'2023 [Paper] [Code]
<details close> <summary>SupReMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/8510f3d8-7ba9-490a-bdbf-439a69ff4bd6" /> </details>
Tailoring Mixup to Data using Kernel Warping functions Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc arXiv'2023 [Paper] [Code]
<details close> <summary>Warped Mixup Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/061f5f2b-ff56-415b-9f58-68e895fdad3f" /> </details>
OmniMixup: Generalize Mixup with Mixing-Pair Sampling Distribution Anonymous Openreview'2023 [Paper]
Augment on Manifold: Mixup Regularization with UMAP Yousef El-Laham, Elizabeth Fons, Dillon Daudert, Svitlana Vyetrenko ICASSP'2024 [Paper]

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Long tail distribution

Remix: Rebalanced Mixup Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan ECCVW'2020 [Paper]
<details close> <summary>Remix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/c776fc89-559f-4e7b-b43a-3e154f3b87c6" /> </details>
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective Zhengzhuo Xu, Zenghao Chai, Chun Yuan NIPS'2021 [Paper] [Code]
<details close> <summary>UniMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/3d7819d1-c2b8-488f-8a6d-8583a66c7f00" /> </details>
Label-Occurrence-Balanced Mixup for Long-tailed Recognition Shaoyu Zhang, Chen Chen, Xiujuan Zhang, Silong Peng ICASSP'2022 [Paper]
<details close> <summary>OBMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/89ce6cd5-8f4a-442f-ab05-4549b04e4cb9" /> </details>
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition Jae Soon Baik, In Young Yoon, Jun Won Choi PR'2024 [Paper]
<details close> <summary>DBN-Mix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/ee7988d0-022e-4155-ac69-5e3feaad4ed9" /> </details>

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Segmentation

ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, Lennart Svensson WACV'2021 [Paper] [Code]
<details close> <summary>ClassMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/a87397e1-c730-4ef6-a48a-3b2189462582" /> </details>
ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation Matheus Barros Pereira, Jefersson Alex dos Santos SIBGRAPI'2021 [Paper]
<details close> <summary>ChessMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/07b0d09d-8d38-4f1a-bb1c-bd5b54dfe770" /> </details>
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision Ke Zhang, Xiahai Zhuang CVPR'2022 [Paper] [Code]
<details close> <summary>CyclesMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/21ee4b1f-255b-4888-a2ec-fb46b7e69122" /> </details>
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation Yi Lin, Zeyu Wang, Kwang-Ting Cheng, Hao Chen MICCAI'2022 [Paper] [Code]
<details close> <summary>InsMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/4dc23fe3-9bf3-4a83-94fc-b03770e414d5" /> </details>
LaserMix for Semi-Supervised LiDAR Semantic Segmentation Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu CVPR'2023 [Paper] [Code] [project]
<details close> <summary>LaserMix Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/209255964-69cab84b-ae54-4e74-be4f-a23a836c665c.png" /> </details>
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Tao Tan, Xinlin Zhang, Tong Tong arXiv'2023 [Paper]
<details close> <summary>DCPA Framework</summary> <img width="50%" src="https://github-production-user-asset-6210df.s3.amazonaws.com/44519745/268410560-a45c03d9-beb1-4b74-a34b-4d1ecd356de9.png" /> </details>
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images Jie Feng, Hao Huang, Junpeng Zhang, Weisheng Dong, Dingwen Zhang, Licheng Jiao arXiv'2024 [Paper] [Code]
<details close> <summary>SA-MixNet Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/a4a564f0-9032-4605-9eee-6f2a4337a94b" /> </details>
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao CVPR'2024 [Paper] [Code]
<details close> <summary>MiDSS Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/1080c789-5dc0-43eb-8e66-61c5eeba2c2f" /> </details>
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather Haimei Zhao, Jing Zhang, Zhuo Chen, Shanshan Zhao, Dacheng Tao CVPR'2024 [Paper] [Code]
ModelMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision Ke Zhang, Vishal M. Patel MICCAI'2024 [Paper]
<details close> <summary>ModelMix Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/9b7a31eb-ddd1-4cad-b15b-52dffb6b52c3" /> </details>

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Object Detection

MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak CVPR'2022 [Paper] [Code]
<details close> <summary>MUM Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/225082975-4143e7f5-8873-433c-ab6f-6caa615f7120.png" /> </details>
Mixed Pseudo Labels for Semi-Supervised Object Detection Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang arXiv'2023 [Paper] [Code]
<details close> <summary>MixPL Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/Awesome-Mixup/assets/44519745/c1ac594f-18bb-465b-b0e7-e96249231e2c" /> </details>
MS-DETR: Efficient DETR Training with Mixed Supervision Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, Jingdong Wang arXiv'2024 [Paper] [Code]
<details close> <summary>MS-DETR Framework</summary> <img width="50%" src="https://github.com/user-attachments/assets/5efea70c-d4f6-4ff8-819b-f68b99fa3c08" /> </details>

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Other Applications

Training Paradigms

Federated Learning

XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning MyungJae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim ICML'2020 [Paper] [Code]
FedMix: Approximation of Mixup Under Mean augmented Federated Learning Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang ECCV'2022 [Paper] [Code]
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup Seungeun Oh, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim IEEE Communications Letters'2020 [Paper]
StatMix: Data augmentation method that relies on image statistics in federated learning Dominik Lewy, Jacek Mańdziuk, Maria Ganzha, Marcin Paprzycki ICONIP'2022 [Paper]

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Adversarial Attack and Adversarial Training

Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training Alfred Laugros, Alice Caplier, Matthieu Ospici ECCV'2020 [Paper]
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang, Kun Xu, Jun Zhu ICLR'2020 [Paper] [Code]
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee, Hyungyu Lee, Sungroh Yoon CVPR'2020 [Paper] [Code]
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li EMNLP'2021 [Paper]
Adversarially Optimized Mixup for Robust Classification Jason Bunk, Srinjoy Chattopadhyay, B. S. Manjunath, Shivkumar Chandrasekaran arXiv'2021 [Paper]
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang ACL'2021 [Paper]
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy Alex Lamb, Vikas Verma, Kenji Kawaguchi, Alexander Matyasko, Savya Khosla, Juho Kannala, Yoshua Bengio NN'2021 [Paper]
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu ICCV'2023 [Paper]
Mixup as directional adversarial training Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang NIPS'2019 [Paper] [Code]
On the benefits of defining vicinal distributions in latent space Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian CVPRW'2021 [Paper]

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Domain Adaption

Mix-up Self-Supervised Learning for Contrast-agnostic Applications Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann ICDE'2022 [Paper]
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das NIPS'2021 [Paper] [Code]
Virtual Mixup Training for Unsupervised Domain Adaptation Xudong Mao, Yun Ma, Zhenguo Yang, Yangbin Chen, Qing Li arXiv'2019 [Paper] [Code]
Improve Unsupervised Domain Adaptation with Mixup Training Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren arXiv'2020 [Paper]
Adversarial Domain Adaptation with Domain Mixup Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang AAAI'2020 [Paper] [Code]
Dual Mixup Regularized Learning for Adversarial Domain Adaptation Yuan Wu, Diana Inkpen, Ahmed El-Roby ECCV'2020 [Paper] [Code]
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation Aadarsh Sahoo, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das WACV'2023 [Paper] [Code]
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan MICCAI'2023 [Paper] [Code]

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Knowledge Distillation

MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li NIPS'2021 [Paper]
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang ECCV'2022 [Paper]
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang WACV'2023 [Paper]

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Multi-Modal

MixGen: A New Multi-Modal Data Augmentation Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li arXiv'2023 [Paper] [Code]
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo ICML'2022 [Paper]
<details close> <summary>VLMixer Framework</summary> <img width="50%" src="https://user-images.githubusercontent.com/44519745/204585605-f60ae9af-b7af-4af4-ac46-28bac51c7a02.png" /> </details>
Geodesic Multi-Modal Mixup for Robust Fine-Tuning Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song NIPS'2023 [Paper] [Code]
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos arXiv'2023 [Paper]
<details close> <summary>PowMix Framework</summary> <img width="50%" src="https://github.com/Westlake-AI/openmixup/assets/44519745/272e44b8-1872-4cdf-9a69-71e48cc428cd" /> </details>
Enhance image classification via inter-class image mixup with diffusion model Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos CVPR'2024 [Paper] [Code]
Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation Keji He, Chenyang Si, Zhihe Lu, Yan Huang, Liang Wang, Xinchao Wang NIPS'2023 [Paper] [Code]

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Beyond Vision

NLP

Augmenting Data with Mixup for Sentence Classification: An Empirical Study Hongyu Guo, Yongyi Mao, Richong Zhang arXiv'2019 [Paper] [Code]
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li EMNLP'2021 [Paper]
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup Hongyu Guo, Yongyi Mao, Richong Zhang EMNLP'2020 [Paper] [Code]
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, Lifang He COLING'2020 [Paper]
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang EMNLP'2020 [Paper] [Code]
Augmenting NLP Models using Latent Feature Interpolations Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah COLING'2020 [Paper]
MixText: Linguistically-informed Interpolation of Hidden Space for Semi-Supervised Text Classification Jiaao Chen, Zichao Yang, Diyi Yang ACL'2020 [Paper] [Code]
Sequence-Level Mixed Sample Data Augmentation Jiaao Chen, Zichao Yang, Diyi Yang EMNLP'2020 [Paper] [Code]
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein ACL'2020 [Paper] [Code]
Local Additivity Based Data Augmentation for Semi-supervised NER Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang ACL'2020 [Paper] [Code]
Mixup Decoding for Diverse Machine Translation Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang EMNLP'2021 [Paper]
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding Le Zhang, Zichao Yang, Diyi Yang NAALC'2022 [Paper] [Code]
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang ACL'2022 [Paper] [Code]
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation Chang Jin, Shigui Qiu, Nini Xiao, Hao Jia IJCAI'2022 [Paper]
Enhancing Cross-lingual Transfer by Manifold Mixup Huiyun Yang, Huadong Chen, Hao Zhou, Lei Li ICLR'2022 [Paper] [Code]
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation Yong Cheng, Ankur Bapna, Orhan Firat, Yuan Cao, Pidong Wang, Wolfgang Macherey ACL'2022 [Paper]

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GNN

Node Augmentation Methods for Graph Neural Network based Object Classification Yifan Xue; Yixuan Liao; Xiaoxin Chen; Jingwei Zhao CDS'2021 [Paper]
Mixup for Node and Graph Classification Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi WWW'2021 [Paper] [Code]
Graph Mixed Random Network Based on PageRank Qianli Ma, Zheng Fan, Chenzhi Wang, Hongye Tan Symmetry'2022 [Paper]
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks Tianxiang Zhao, Xiang Zhang, Suhang Wang WSDM'2021 [Paper]
GraphMix: Improved Training of GNNs for Semi-Supervised Learning Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang AAAI'2021 [Paper] [Code]
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li ECML-PKDD'2022 [Paper] [Code]
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation Joonhyung Park, Hajin Shim, Eunho Yang AAAI'2022 [Paper] [Code]
G-Mixup: Graph Data Augmentation for Graph Classification Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu ICML'2022 [Paper]
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, Wenwu Zhu NIPS'2023 [Paper] [code]
iGraphMix: Input Graph Mixup Method for Node Classification Jongwon Jeong, Hoyeop Lee, Hyui Geon Yoon, Beomyoung Lee, Junhee Heo, Geonsoo Kim, Kim Jin Seon ICLR'2024 [Paper]

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3D Point

PointMixup: Augmentation for Point Clouds Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, Cees G.M. Snoek ECCV'2020 [Paper] [Code]
PointCutMix: Regularization Strategy for Point Cloud Classification Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu Neurocomputing'2022 [Paper] [Code]
Regularization Strategy for Point Cloud via Rigidly Mixed Sample Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee CVPR'2021 [Paper] [Code]
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud Jaeseok Choi, Yeji Song, Nojun Kwak IROS'2021 [Paper] [Code]
Point MixSwap: Attentional Point Cloud Mixing via Swapping Matched Structural Divisions Ardian Umam, Cheng-Kun Yang, Yung-Yu Chuang, Jen-Hui Chuang, Yen-Yu Lin ECCV'2022 [Paper] [Code]

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Other

Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning Byungsoo Ko, Geonmo Gu CVPR'2020 [Paper] [Code]
SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, Heechul Jung Sensor'2021 [Paper]
Octave Mix: Data Augmentation Using Frequency Decomposition for Activity Recognition Tatsuhito Hasegawa IEEE Access'2021 [Paper]
Guided Interpolation for Adversarial Training Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama arXiv'2021 [Paper]
Recall@k Surrogate Loss with Large Batches and Similarity Mixup Yash Patel, Giorgos Tolias, Jiri Matas CVPR'2022 [Paper] [Code]
Contrastive-mixup Learning for Improved Speaker Verification Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke ICASSP'2022 [Paper]
Noisy Feature Mixup Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney ICLR'2022 [Paper] [Code]
It Takes Two to Tango: Mixup for Deep Metric Learning Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis ICLR'2022 [Paper] [Code]
Representational Continuity for Unsupervised Continual Learning Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang ICLR'2022 [Paper] [Code]
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding Remy Sun, Clement Masson, Gilles Henaff, Nicolas Thome, Matthieu Cord. ICPR'2022 [Paper]
Guarding Barlow Twins Against Overfitting with Mixed Samples Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel arXiv'2023 [Paper] [Code]
Infinite Class Mixup Thomas Mensink, Pascal Mettes arXiv'2023 [Paper]
Semantic Equivariant Mixup Zongbo Han, Tianchi Xie, Bingzhe Wu, Qinghua Hu, Changqing Zhang arXiv'2023 [Paper]
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima Xingyu Li, Bo Tang arXiv'2023 [Paper]
Inter-Instance Similarity Modeling for Contrastive Learning Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang arXiv'2023 [Paper] [Code]
Single-channel speech enhancement using learnable loss mixup Oscar Chang, Dung N. Tran, Kazuhito Koishida arXiv'2023 [Paper]
Selective Volume Mixup for Video Action Recognition Yi Tan, Zhaofan Qiu, Yanbin Hao, Ting Yao, Xiangnan He, Tao Mei arXiv'2023 [Paper]
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn CVPR'2020 & IJCV'2024 [Paper] [Code]
DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu arXiv'2024 [Paper] [Code]
ContextMix: A context-aware data augmentation method for industrial visual inspection systems Hyungmin Kim, Donghun Kim, Pyunghwan Ahn, Sungho Suh, Hansang Cho, Junmo Kim EAAI'2024 [Paper]
Robust Image Denoising through Adversarial Frequency Mixup Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han CVPR'2024 [Paper] [Code]

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Analysis and Theorem

Understanding Mixup Training Methods Daojun Liang, Feng Yang, Tian Zhang, Peter Yang NIPS'2019 [Paper]
MixUp as Locally Linear Out-Of-Manifold Regularization Hongyu Guo, Yongyi Mao, Richong Zhang AAAI'2019 [Paper]
MixUp as Directional Adversarial Training Chanwoo Park, Sangdoo Yun, Sanghyuk Chun NIPS'2019 [Paper]
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak NIPS'2019 [Paper] [Code]
On Mixup Regularization Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert arXiv'2020 [Paper]
Mixup Training as the Complexity Reduction Masanari Kimura arXiv'2021 [Paper]
How Does Mixup Help With Robustness and Generalization Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou ICLR'2021 [Paper]
Mixup Without Hesitation Hao Yu, Huanyu Wang, Jianxin Wu ICIG'2022 [Paper] [Code]
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania NIPS'2022 [Paper] [Code]
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective Chanwoo Park, Sangdoo Yun, Sanghyuk Chun NIPS'2022 [Paper] [Code]
Towards Understanding the Data Dependency of Mixup-style Training Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge ICLR'2022 [Paper] [Code]
When and How Mixup Improves Calibration Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou ICML'2022 [Paper]
Provable Benefit of Mixup for Finding Optimal Decision Boundaries Junsoo Oh, Chulhee Yun ICML'2023 [Paper]
On the Pitfall of Mixup for Uncertainty Calibration Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang CVPR'2023 [Paper]
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga WACV'2023 [Paper] [Code]
Over-Training with Mixup May Hurt Generalization Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao ICLR'2023 [Paper]
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability Soyoun Won, Sung-Ho Bae, Seong Tae Kim arXiv'2023 [Paper]
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup Damien Teney, Jindong Wang, Ehsan Abbasnejad ICML'2024 [Paper]
Pushing Boundaries: Mixup's Influence on Neural Collapse Quinn Fisher, Haoming Meng, Vardan Papyan ICLR'2024 [Paper]

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Survey

A survey on Image Data Augmentation for Deep Learning Connor Shorten and Taghi Khoshgoftaar Journal of Big Data'2019 [Paper]
An overview of mixing augmentation methods and augmentation strategies Dominik Lewy and Jacek Ma ́ndziuk Artificial Intelligence Review'2022 [Paper]
Image Data Augmentation for Deep Learning: A Survey Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, Furao Shen arXiv'2022 [Paper]
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang arXiv'2022 [Paper] [Code]
A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate Tsz-Him Cheung, Dit-Yan Yeung IEEE TNNLS'2023 [Paper]
Survey: Image Mixing and Deleting for Data Augmentation Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, Ajmal Mian EAAI'2024 [Paper]
A Survey on Mixup Augmentations and Beyond Xin Jin, Hongyu Zhu, Siyuan Li, Zedong Wang, Zecheng Liu, Chang Yu, Huafeng Qin, Stan. Z. Li arXiv'2024 [Paper]

Benchmark

OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Weiyang Jin, Stan Z. Li arXiv'2024 [Paper] [Code]

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Classification Results on Datasets

Mixup methods classification results on general datasets: CIFAR10 \ CIFAR100, TinyImageNet, and ImageNet-1K. $(\cdot)$ denotes training epochs based on ResNet18 (R18), ResNet50 (R50), ResNeXt50 (RX50), PreActResNet18 (PreActR18), and Wide-ResNet28 (WRN28-10, WRN28-8).

Method	Publish	CIFAR10	CIFAR100	CIFAR100	CIFAR100	CIFAR100	CIFAR100	Tiny-ImageNet	Tiny-ImageNet	ImageNet-1K	ImageNet-1K
		R18	R18	RX50	PreActR18	WRN28-10	WRN28-8	R18	RX50	R18	R50
MixUp	ICLR'2018	96.62(800)	79.12(800)	82.10(800)	78.90(200)	82.50(200)	82.82(400)	63.86(400)	66.36(400)	69.98(100)	77.12(100)
CutMix	ICCV'2019	96.68(800)	78.17(800)	78.32(800)	76.80(1200)	83.40(200)	84.45(400)	65.53(400)	66.47(400)	68.95(100)	77.17(100)
Manifold Mixup	ICML'2019	96.71(800)	80.35(800)	82.88(800)	79.66(1200)	81.96(1200)	83.24(400)	64.15(400)	67.30(400)	69.98(100)	77.01(100)
FMix	arXiv'2020	96.18(800)	79.69(800)	79.02(800)	79.85(200)	82.03(200)	84.21(400)	63.47(400)	65.08(400)	69.96(100)	77.19(100)
SmoothMix	CVPRW'2020	96.17(800)	78.69(800)	78.95(800)	-	-	82.09(400)	-	-	-	77.66(300)
GridMix	PR'2020	96.56(800)	78.72(800)	78.90(800)	-	-	84.24(400)	64.79(400)	-	-	-
ResizeMix	arXiv'2020	96.76(800)	80.01(800)	80.35(800)	-	85.23(200)	84.87(400)	63.47(400)	65.87(400)	69.50(100)	77.42(100)
SaliencyMix	ICLR'2021	96.20(800)	79.12(800)	78.77(800)	80.31(300)	83.44(200)	84.35(400)	64.60(400)	66.55(400)	69.16(100)	77.14(100)
Attentive-CutMix	ICASSP'2020	96.63(800)n	78.91(800)	80.54(800)	-	-	84.34(400)	64.01(400)	66.84(400)	-	77.46(100)
Saliency Grafting	AAAI'2022	-	80.83(800)	83.10(800)	-	84.68(300)	-	64.84(600)	67.83(400)	-	77.65(100)
PuzzleMix	ICML'2020	97.10(800)	81.13(800)	82.85(800)	80.38(1200)	84.05(200)	85.02(400)	65.81(400)	67.83(400)	70.12(100)	77.54(100)
Co-Mix	ICLR'2021	97.15(800)	81.17(800)	82.91(800)	80.13(300)	-	85.05(400)	65.92(400)	68.02(400)	-	77.61(100)
SuperMix	CVPR'2021	-	-	-	79.07(2000)	93.60(600)	-	-	-	-	77.60(600)
RecursiveMix	NIPS'2022	-	81.36(200)	-	80.58(2000)	-	-	-	-	-	79.20(300)
AutoMix	ECCV'2022	97.34(800)	82.04(800)	83.64(800)	-	-	85.18(400)	67.33(400)	70.72(400)	70.50(100)	77.91(100)
SAMix	arXiv'2021	97.50(800)	82.30(800)	84.42(800)	-	-	85.50(400)	68.89(400)	72.18(400)	70.83(100)	78.06(100)
AlignMixup	CVPR'2022	-	-	-	81.71(2000)	-	-	-	-	-	78.00(100)
MultiMix	NIPS'2023	-	-	-	81.82(2000)	-	-	-	-	-	78.81(300)
GuidedMixup	AAAI'2023	-	-	-	81.20(300)	84.02(200)	-	-	-	-	77.53(100)
Catch-up Mix	AAAI'2023	-	82.10(400)	83.56(400)	82.24(2000)	-	-	68.84(400)	-	-	78.71(300)
LGCOAMix	TIP'2024	-	82.34(800)	84.11(800)	-	-	-	68.27(400)	73.08(400)	-	-
AdAutoMix	ICLR'2024	97.55(800)	82.32(800)	84.42(800)	-	-	85.32(400)	69.19(400)	72.89(400)	70.86(100)	78.04(100)

Mixup methods classification results on ImageNet-1K dataset use ViT-based models: DeiT, Swin Transformer (Swin), Pyramid Vision Transformer (PVT), and ConvNext trained 300 epochs.

Method	Publish	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K
		DieT-Tiny	DieT-Small	DieT-Base	Swin-Tiny	PVT-Tiny	PVT-Small	ConvNeXt-Tiny
MixUp	ICLR'2018	74.69	77.72	78.98	81.01	75.24	78.69	80.88
CutMix	ICCV'2019	74.23	80.13	81.61	81.23	75.53	79.64	81.57
FMix	arXiv'2020	74.41	77.37	-	79.60	75.28	78.72	81.04
ResizeMix	arXiv'2020	74.79	78.61	80.89	81.36	76.05	79.55	81.64
SaliencyMix	ICLR'2021	74.17	79.88	80.72	81.37	75.71	79.69	81.33
Attentive-CutMix	ICASSP'2020	74.07	80.32	82.42	81.29	74.98	79.84	81.14
PuzzleMix	ICML'2020	73.85	80.45	81.63	81.47	75.48	79.70	81.48
AutoMix	ECCV'2022	75.52	80.78	82.18	81.80	76.38	80.64	82.28
SAMix	arXiv'2021	75.83	80.94	82.85	81.87	76.60	80.78	82.35
TransMix	CVPR'2022	74.56	80.68	82.51	81.80	75.50	80.50	-
TokenMix	ECCV'2022	75.31	80.80	82.90	81.60	75.60	-	73.97
TL-Align	ICCV'2023	73.20	80.60	82.30	81.40	75.50	80.40	-
SMMix	ICCV'2023	75.56	81.10	82.90	81.80	75.60	81.03	-
Mixpro	ICLR'2023	73.80	81.30	82.90	82.80	76.70	81.20	-
LUMix	ICASSP'2024	-	80.60	80.20	81.70	-	-	82.50

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Dataset	Type	Label	Task	Total data number	Link
MINIST	Image	10	Classification	70,000	MINIST
Fashion-MNIST	Image	10	Classification	70,000	Fashion-MINIST
CIFAR10	Image	10	Classification	60,000	CIFAR10
CIFAR100	Image	100	Classification	60,000	CIFAR100
SVHN	Image	10	Classification	630,420	SVHN
GTSRB	Image	43	Classification	51,839	GTSRB
STL10	Image	10	Classification	113,000	STL10
Tiny-ImageNet	Image	200	Classification	100,000	Tiny-ImageNet
ImageNet-1K	Image	1,000	Classification	1,431,167	ImageNet-1K
CUB-200-2011	Image	200	Classification, Object Detection	11,788	CUB-200-2011
FGVC-Aircraft	Image	102	Classification	10,200	FGVC-Aircraft
StanfordCars	Image	196	Classification	16,185	StanfordCars
Oxford Flowers	Image	102	Classification	8,189	Oxford Flowers
Caltech101	Image	101	Classification	9,000	Caltech101
SOP	Image	22,634	Classification	120,053	SOP
Food-101	Image	101	Classification	101,000	Food-101
SUN397	Image	899	Classification	130,519	SUN397
iNaturalist	Image	5,089	Classification	675,170	iNaturalist
CIFAR-C	Image	10,100	Corruption Classification	60,000	CIFAR-C
CIFAR-LT	Image	10,100	Long-tail Classification	60,000	CIFAR-LT
ImageNet-1K-C	Image	1,000	Corruption Classification	1,431,167	ImageNet-1K-C
ImageNet-A	Image	200	Classification	7,500	ImageNet-A
Pascal VOC 102	Image	20	Object Detection	33,043	Pascal VOC 102
MS-COCO Detection	Image	91	Object Detection	164,062	MS-COCO Detection
DSprites	Image	737,280*6	Disentanglement	737,280	DSprites
Place205	Image	205	Recognition	2,500,000	Place205
Pascal Context	Image	459	Segmentation	10,103	Pascal Context
ADE20K	Image	3,169	Segmentation	25,210	ADE20K
Cityscapes	Image	19	Segmentation	5,000	Cityscapes
StreetHazards	Image	12	Segmentation	7,656	StreetHazards
PACS	Image	7*4	Domain Classification	9,991	PACS
BRACS	Medical Image	7	Classification	4,539	BRACS
BACH	Medical Image	4	Classification	400	BACH
CAME-Lyon16	Medical Image	2	Anomaly Detection	360	CAME-Lyon16
Chest X-Ray	Medical Image	2	Anomaly Detection	5,856	Chest X-Ray
BCCD	Medical Image	4,888	Object Detection	364	BCCD
TJU600	Palm-Vein Image	600	Classification	12,000	TJU600
VERA220	Palm-Vein Image	220	Classification	2,200	VERA220
CoNLL2003	Text	4	Classification	2,302	CoNLL2003
20 Newsgroups	Text	20	OOD Detection	20,000	20 Newsgroups
WOS	Text	134	OOD Detection	46,985	WOS
SST-2	Text	2	Sentiment Understanding	68,800	SST-2
Cora	Graph	7	Node Classification	2,708	Cora
Citeseer	Graph	6	Node Classification	3,312	Citeseer
PubMed	Graph	3	Node Classification	19,717	PubMed
BlogCatalog	Graph	39	Node Classification	10,312	BlogCatalog
Google Commands	Speech	30	Classification	65,000	Google Commands
VoxCeleb2	Speech	6,112	Sound Classification	1,000,000+	VoxCeleb2
VCTK	Speech	110	Enhancement	44,000	VCTK
ModelNet40	3D Point Cloud	40	Classification	12,311	ModelNet40
ScanObjectNN	3D Point Cloud	15	Classification	15,000	ScanObjectNN
ShapeNet	3D Point Cloud	16	Recognition, Classification	16,880	ShapeNet
KITTI360	3D Point Cloud	80,256	Detection, Segmentation	14,999	KITTI360
UCF101	Video	101	Action Recognition	13,320	UCF101
Kinetics400	Video	400	Action Recognition	260,000	Kinetics400
Airfoil	Tabular	-	Regression	1,503	Airfoil
NO2	Tabular	-	Regression	500	NO2
Exchange-Rate	Timeseries	-	Regression	7,409	Exchange-Rate
Electricity	Timeseries	-	Regression	26,113	Electricity

Contribution

Feel free to send pull requests to add more links with the following Markdown format. Note that the abbreviation, the code link, and the figure link are optional attributes.

* **TITLE**<br>
*AUTHER*<br>
PUBLISH'YEAR [[Paper](link)] [[Code](link)]
   <details close>
   <summary>ABBREVIATION Framework</summary>
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Citation

If you feel that our work has contributed to your research, please cite it, thanks. 🥰

@article{jin2024survey,
  title={A Survey on Mixup Augmentations and Beyond},
  author={Jin, Xin and Zhu, Hongyu and Li, Siyuan and Wang, Zedong and Liu, Zicheng and Yu, Chang and Qin, Huafeng and Li, Stan Z},
  journal={arXiv preprint arXiv:2409.05202},
  year={2024}
}

Current contributors include: Siyuan Li (@Lupin1998), Xin Jin (@JinXins), Zicheng Liu (@pone7), and Zedong Wang (@Jacky1128). We thank all contributors for Awesome-Mixup!

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License

This project is released under the Apache 2.0 license.

Acknowledgement

This repository is built using the OpenMixup library and Awesome README repository.

Related Project

OpenMixup: CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark.
Awesome-Mix: An awesome list of papers for A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability, we categorize them based on our proposed taxonomy.
survery-image-mixing-and-deleting-for-data-augmentation: An awesome list of papers for Survey: Image Mixing and Deleting for Data Augmentation.
awesome-mixup: A collection of awesome papers about mixup.
awesome-mixed-sample-data-augmentation: A collection of awesome things about mixed sample data augmentation.
data-augmentation-review: List of useful data augmentation resources.
Awesome-Mixup: An awesome list of papers for A Survey on Mixup Augmentations and Beyond.

Awesome

Awesome-Mixup

Introduction

Figuer of Contents

Table of Contents

Sample Mixup Policies in SL

Static Linear

Feature-based

Cutting-based

K Samples Mixup

Random Policies

Style-based

Saliency-based

Attention-based

Generating Samples

Label Mixup Policies in SL

Optimizing Calibration

Area-based

Loss Object

Random Label Policies

Optimizing Mixing Ratio

Generating Label

Attention Score

Saliency Token

Self-Supervised Learning

Contrastive Learning

Masked Image Modeling

Semi-Supervised Learning

CV Downstream Tasks

Regression

Long tail distribution

Segmentation

Object Detection

Other Applications

Training Paradigms

Federated Learning

Adversarial Attack and Adversarial Training

Domain Adaption

Knowledge Distillation

Multi-Modal

Beyond Vision

NLP

GNN

3D Point

Other

Analysis and Theorem

Survey

Benchmark

Classification Results on Datasets

Related Datasets Link

Contribution

Citation

License

Acknowledgement

Related Project