Self-Adaptive Training for Selective Classification

This repository contains the PyTorch implementation of Selective Classification used in the

NeurIPS'2020 paper Self-Adaptive Training: beyond Empirical Risk Minimization，
Journal version Self-Adaptive Training: Bridging the Supervised and Self-Supervised Learning.

Self-adaptive training significantly improves the generalization of deep networks under noise and enhances the self-supervised representation learning. It also advances the state-of-the-art on learning with noisy label, adversarial training and the linear evaluation on the learned representation.

News

2021.10: We have released the code for Selective Classification.
2021.01: We have released the journal version of Self-Adaptive Training, which is a unified algorithm for both the supervised and self-supervised learning. Code for self-supervised learning will be available soon.
2020.09: Our work has been accepted at NeurIPS'2020.

Requirements

Python >= 3.6
PyTorch >= 1.0
CUDA
Numpy

Usage

Training and evaluating Selective Classification based on SAT

The main.py contains training and evaluation functions in standard training setting.

Runnable scripts

Training and evaluation using the default parameters

We provide our training scripts in directory train.sh. For a concrete example, we can use the command as below to train the default model (i.e., VGG16-BN) on CIFAR10 dataset:
```
$ bash train.sh
```
Additional arguments
- arch: the architecture of backbone model, e.g., vgg16_bn
- dataset: the trainiing dataset, e.g., cifar10
- loss: the loss function for training, e.g., sat
- sat-momentum: the momentum term of our approach

Results on CIFAR10, SVHN and Dogs&Cats datasets under various coverage rate

Self-Adaptive Training vs. prior state-of-the-arts on the Selective Classification at vaiours coverage rate.

Reference

For technical details, please check the conference version or the journal version of our paper.

@inproceedings{huang2020self,
  title={Self-Adaptive Training: beyond Empirical Risk Minimization},
  author={Huang, Lang and Zhang, Chao and Zhang, Hongyang},
  booktitle={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@article{huang2021self,
  title={Self-Adaptive Training: Bridging the Supervised and Self-Supervised Learning},
  author={Huang, Lang and Zhang, Chao and Zhang, Hongyang},
  journal={arXiv preprint arXiv:2101.08732},
  year={2021}
}

Acknowledgement

This code is based on:

We thank the authors for sharing their code.

Contact

If you have any question about this code, feel free to open an issue or contact laynehuang@pku.edu.cn.

LayneH / SAT-selective-cls

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