Self-Adaptive Training

This is the PyTorch implementation of 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: The code of Selective Classification for SAT has been released here.
• 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

Standard training

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 scripts/. For a concrete example, we can use the command as below to train the default model (i.e., ResNet-34) on CIFAR10 dataset with uniform label noise injected (e.g., 40%):

  $ bash scripts/cifar10/run_sat.sh [TRIAL_NAME]
  

  The argument TRIAL_NAME is optional, it helps us to identify different trials of the same experiments without modifying the training script. The evaluation is automatically performed when training is finished.

• Additional arguments

  • noise-rate: the percentage of data that being corrupted
  • noise-type: type of random corruptions (i.e., corrupted_label, Gaussian,random_pixel, shuffled_pixel)
  • sat-es: initial epochs of our approach
  • sat-alpha: the momentum term $\alpha$ of our approach
  • arch: the architecture of backbone model, e.g., resnet34/wrn34

Results on CIFAR datasets under uniform label noise

• Test Accuracy(%) on CIFAR10

• Test Accuracy(%) on CIFA100

Runnable scripts for repreducing double-descent phenomenon

You can use the command as below to train the default model (i.e., ResNet-18) on CIFAR10 dataset with 16.67% uniform label noise injected (i.e., 15% label error rate):

$ bash scripts/cifar10/run_sat_dd_parallel.sh [TRIAL_NAME]
$ bash scripts/cifar10/run_ce_dd_parallel.sh [TRIAL_NAME]

Double-descent ERM vs. single-descent self-adaptive training

Double-descent ERM vs. single-descent self-adaptive training on the error-capacity curve. The vertical dashed line represents the interpolation threshold.

Double-descent ERM vs. single-descent self-adaptive training on the epoch-capacity curve. The dashed vertical line represents the initial epoch E_s of our approach.

Adversarial training

We use state-of-the-art adversarial training algorithm TRADES as our baseline. The main_adv.py contains training and evaluation functions in adversarial training setting on CIFAR10 dataset.

Training scripts

• Training and evaluation using the default parameters

  We provides our training scripts in directory scripts/cifar10. For a concrete example, we can use the command as below to train the default model (i.e., WRN34-10) on CIFAR10 dataset with PGD-10 attack ($\epsilon$=0.031) to generate adversarial examples:

  $ bash scripts/cifar10/run_trades_sat.sh [TRIAL_NAME]
  

• Additional arguments

  • beta: hyper-parameter $1/\lambda$ in TRADES that controls the trade-off between natural accuracy and adversarial robustness
  • sat-es: initial epochs of our approach
  • sat-alpha: the momentum term $\alpha$ of our approach

Robust evaluation script

Evaluate robust WRN-34-10 models on CIFAR10 under PGD-20 attack:

  $ python pgd_attack.py --model-dir "/path/to/checkpoints"

This command evaluates 71-st to 100-th checkpoints in the specified path.

Results

Self-Adaptive Training mitigates the overfitting issue and consistently improves TRADES.

Attack TRADES+SAT

We provide the checkpoint of our best performed model in Google Drive and compare its natural and robust accuracy with TRADES as below.

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}
}

Contact

If you have any question about this code, feel free to open an issue or contact [email protected].