Awesome
NNM: Nearest Neighbor Matching for Deep Clustering.
Forked from SCAN (https://github.com/wvangansbeke/Unsupervised-Classification).
Introduction
<img src="images/ill_idea.jpg" width="400" /> <p> The illustration of our idea. We propose to match more semantically nearest neighbors from between <b>local (batch)</b> and <b>global (overall)</b> level. Benefit from the dynamic updated deep features with iteration and epoch increases, we can construct more and more semantically confident sample pairs from samples and its neighbors. </p>Framework
Local Nearest Neighbor Matching
<img src="images/local_loss.jpg" width="400" />Global Nearest Neighbor Matching
<img src="images/global_loss.jpg" width="400" />For specifical loss, please refer paper and poster.
Main Results
<img src="images/results.jpg" width="800" />Pre-Trained Models
| Models | Links |
|---|---|
| CIFAR-10 | Google Drive |
| CIFAR-20 | Google Drive |
| STL-10 | Google Drive |
Trained Models
| Models | ACC | Links |
|---|---|---|
| CIFAR-10 | 0.8430 | Google Drive |
| CIFAR-20 | 0.4773 | Google Drive |
| STL-10 | 0.8084 | Google Drive |
Run
Requirements
Python 3.7 and Pytorch 1.4.0 are required. Please refer to requirements.yaml for more details.
Usages
Clone this repo: git clone https://github.com/ZhiyuanDang/NNM.git.
Download datasets: CIFAR-10/100, STL-10.
We can directly use the pre-text model from SCAN. Then, we only need to generate the neighbors by the code
python simclr.py --config_env configs/env.yml --config_exp configs/pretext/simclr_cifar10.yml.
Next, we run the clustering step (optional):
python scan.py --config_env configs/env.yml --config_exp configs/scan/scan_cifar10.yml --gpus 0 (--seed 1234).
Visualizing the top-k images is easily done by setting the --visualize_prototypes flag.
For example on cifar-10:
python eval.py --config_exp configs/scan/scan_cifar10.yml --model $MODEL_PATH --visualize_prototypes.
And the Top-3 images is: <img src="images/protype-cifar10.jpg" width="800" />
However, due to issues in SCAN, self-label is not suitable for NNM. Thus, we remove this file.
Citation
@InProceedings{Dang_2021_CVPR,
author = {Dang, Zhiyuan and Deng, Cheng and Yang, Xu and Wei, Kun and Huang, Heng},
title = {Nearest Neighbor Matching for Deep Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13693-13702}
}