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Using Pre-Training Can Improve Model Robustness and Uncertainty

This repository contains the essential code for the paper Using Pre-Training Can Improve Model Robustness and Uncertainty, ICML 2019.

Requires Python 3+ and PyTorch 0.4.1+.

Abstract

Kaiming He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance, should the model train long enough. We show that although pre-training may not improve performance on traditional classification metrics, it does provide large benefits to model robustness and uncertainty. With pre-training, we show approximately a 30% relative improvement in label noise robustness and a 10% absolute improvement in adversarial robustness on CIFAR-10 and CIFAR-100. Pre-training also improves model calibration. In some cases, using pre-training without task-specific methods surpasses the state-of-the-art, highlighting the importance of using pre-training when evaluating future methods on robustness and uncertainty tasks.

Citation

If you find this useful in your research, please consider citing:

@article{hendrycks2019pretraining,
  title={Using Pre-Training Can Improve Model Robustness and Uncertainty},
  author={Hendrycks, Dan and Lee, Kimin and Mazeika, Mantas},
  journal={Proceedings of the International Conference on Machine Learning},
  year={2019}
}

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