Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

Andy Zhou, Jindong Wang, Yu-Xiong Wang, Haohan Wang

Keywords: robustness, knowledge distillation, adversarial training, data augmentation, generalization

TL;DR: We distill out-of-distribution robustness from foundation models by generating adversarial examples from the teacher

Abstract: We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation. We address the conjecture that larger models do not make for better teachers by showing strong gains in out-of-distribution robustness when distilling from pretrained foundation models. Following this finding, we propose Discrete Adversarial Distillation (DAD), which leverages a robust teacher to generate adversarial examples and a VQGAN to discretize them, creating more informative samples than standard data augmentation techniques. We provide a theoretical framework for the use of a robust teacher in the knowledge distillation with data augmentation setting and demonstrate strong gains in out-of-distribution robustness and clean accuracy across different student architectures. Notably, our method adds minor computational overhead compared to similar techniques and can be easily combined with other data augmentations for further improvements.

Submission Number: 8736