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arXiv · 2409.12946

Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation

Tsung-Han Wu·Hung-Ting Su·Shang-Tse Chen·Winston H. Hsu
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Abstract

The robust self-training (RST) framework has emerged as a prominent approach for semi-supervised adversarial training. To explore the possibility of tackling more complicated tasks with even lower labeling budgets, unlike prior approaches that rely on robust pretrained models, we present SNORD - a simple yet effective framework that introduces contemporary semi-supervised learning techniques into the realm of adversarial training. By enhancing pseudo labels and managing noisy training data more effectively, SNORD showcases impressive, state-of-the-art performance across diverse datasets and labeling budgets, all without the need for pretrained models. Compared to full adversarial supervision, SNORD achieves a 90% relative robust accuracy under epsilon = 8/255 AutoAttack, requiring less than 0.1%, 2%, and 10% labels for CIFAR-10, CIFAR-100, and TinyImageNet-200, respectively. Additional experiments confirm the efficacy of each component and demonstrate the adaptability of integrating SNORD with existing adversarial pretraining strategies to further bolster robustness.

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Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen, Winston H. Hsu. 2024-09-19. Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation. https://arxiv.org/abs/2409.12946

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