Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution. However, the ability to tailor such strategies to inoculate a model against specific classes of corruptions or attacks -- without incurring substantial losses in robustness against other classes of corruptions -- remains elusive. In this work, we successfully harden a model against Fourier-based attacks, while producing superior-to-AugMix accuracy and calibration results on both the CIFAR-10-C and CIFAR-100-C datasets; classification error is reduced by over ten percentage points for some high-severity noise and digital-type corruptions. We achieve this by incorporating Fourier-basis perturbations in the AugMix image-augmentation framework. Thus we demonstrate that the AugMix framework can be tailored to effectively target particular distribution shifts, while boosting overall model robustness.
@article{arxiv.2202.12412,
title = {Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration},
author = {Ryan Soklaski and Michael Yee and Theodoros Tsiligkaridis},
journal= {arXiv preprint arXiv:2202.12412},
year = {2022}
}
Comments
35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia