Awesome
ISDA-Pytorch
The Implicit Semantic Data Augmentation (ISDA) algorithm implemented in Pytorch.
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(NeurIPS 2019) Implicit Semantic Data Augmentation for Deep Networks
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(T-PAMI) Regularizing Deep Networks with Semantic Data Augmentation
Update on 2021/04/23: Release Code for Visualizing Deep Features on ImageNet!
Update on 2021/01/17: Journal Version of ISDA is Accepted by T-PAMI!
Update on 2020/04/25: Release Pre-trained Models on ImageNet.
Update on 2020/04/24: Release Code for Image Classification on ImageNet and Semantic Segmentation on Cityscapes.
Introduction
We propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. ISDA consistently improves the generalization performance of popular deep networks on supervised & semi-supervised image classification, semantic segmentation, object detection and instance segmentation.
<p align="center"> <img src="ISDA-overview.png" width= "900"> </p>Citation
If you find this work valuable or use our code in your own research, please consider citing us with the following bibtex:
@inproceedings{NIPS2019_9426,
title = {Implicit Semantic Data Augmentation for Deep Networks},
author = {Wang, Yulin and Pan, Xuran and Song, Shiji and Zhang, Hong and Huang, Gao and Wu, Cheng},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
pages = {12635--12644},
year = {2019},
}
@article{wang2021regularizing,
title = {Regularizing deep networks with semantic data augmentation},
author = {Wang, Yulin and Huang, Gao and Song, Shiji and Pan, Xuran and Xia, Yitong and Wu, Cheng},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2021},
doi = {10.1109/TPAMI.2021.3052951}
}
Get Started
Please go to the folder Image classification on CIFAR, Image classification on ImageNet, Semantic segmentation on Cityscapes and Visualizing deep features for specific docs.
Pre-trained Models on ImageNet
- Measured by Top-1 error.
Model | Params | Baseline | ISDA | Model |
---|---|---|---|---|
ResNet-50 | 25.6M | 23.0 | 21.9 | Tsinghua Cloud / Google Drive |
ResNet-101 | 44.6M | 21.7 | 20.8 | Tsinghua Cloud / Google Drive |
ResNet-152 | 60.3M | 21.3 | 20.3 | Tsinghua Cloud / Google Drive |
DenseNet-BC-121 | 8.0M | 23.7 | 23.2 | Tsinghua Cloud / Google Drive |
DenseNet-BC-265 | 33.3M | 21.9 | 21.2 | Tsinghua Cloud / Google Drive |
ResNeXt50, 32x4d | 25.0M | 22.5 | 21.3 | Tsinghua Cloud / Google Drive |
ResNeXt101, 32x8d | 88.8M | 21.1 | 20.1 | Tsinghua Cloud / Google Drive |
Visualization of Augmented Samples
- ImageNet
Results
- Supervised image classification on ImageNet
- Complementing traditional data augmentation techniques
- Semi-supervised image classification on CIFAR & SVHN
- Semantic segmentation on Cityscapes
- Object detection on MS COCO
- Instance segmentation on MS COCO
Acknowledgment
Our code for semantic segmentation is mainly based on pytorch-segmentation-toolbox.
To Do
Update code for semi-supervised learning.