Improving Unsupervised Image Clustering With Robust Learning
This repo is the PyTorch codes for "Improving Unsupervised Image Clustering With Robust Learning (RUC)"
Improving Unsupervised Image Clustering With Robust Learning
Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha.
Highlight
- Accepted at CVPR 2021.
-
🏆 SOTA on 4 benchmarks. Check out Papers With Code for Image Clustering or Unsup. Classification.
- RUC is an add-on module to enhance the performance of any off-the-shelf unsupervised learning algorithms. RUC is inspired by robust learning. It first divides clustered data points into clean and noisy set, then refine the clustering results. With RUC, state-of-the-art unsupervised clustering methods; SCAN and TSUC showed showed huge performance improvements. (STL-10 : 86.7%, CIFAR-10 : 90.3%, CIFAR-20 : 54.3%)
- Prediction results of existing unsupervised learning algorithms were overconfident. RUC can make the prediction of existing algorithms softer with better calibration.
- Robust to adversarially crafted samples. ERM-based unsupervised clustering algorithms can be prone to adversarial attack. Adding RUC to the clustering models improves robustness against adversarial noise.
- Robust to adversarially crafted samples. ERM-based unsupervised clustering algorithms can be prone to adversarial attack. Adding RUC to the clustering models improves robustness against adversarial noise.
Required packages
- python == 3.6.10
- pytorch == 1.1.0
- scikit-learn == 0.21.2
- scipy == 1.3.0
- numpy == 1.18.5
- pillow == 7.1.2
Overall model architecture
Usage
usage: main_ruc_[dataset].py [-h] [--lr LR] [--momentum M] [--weight_decay W]
[--epochs EPOCHS] [--batch_size B] [--s_thr S_THR]
[--n_num N_NUM] [--o_model O_MODEL]
[--e_model E_MODEL] [--seed SEED]
config for RUC
optional arguments:
-h, --help show this help message and exit
--lr LR initial learning rate
--momentum M momentum
--weight_decay weight decay
--epochs EPOCHS max epoch per round. (default: 200)
--batch_size B training batch size
--s_thr S_THR confidence sampling threshold
--n_num N_NUM the number of neighbor for metric sampling
--o_model O_MODEL original model path
--e_model E_MODEL embedding model path
--seed SEED random seed
Model ZOO
Currently, we support the pretrained model for our model. We used the pretrained SCAN and SimCLR model from SCAN github.
| Dataset | Download link |
|---|---|
| CIFAR-10 | Download |
| CIFAR-20 | Download |
| STL-10 | Download |
Citation
If you find this repo useful for your research, please consider citing our paper:
@article{park2020improving,
title={Improving Unsupervised Image Clustering With Robust Learning},
author={Park, Sungwon and Han, Sungwon and Kim, Sundong and Kim, Danu and Park, Sungkyu and Hong, Seunghoon and Cha, Meeyoung},
journal={arXiv preprint arXiv:2012.11150},
year={2020}
}
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