Web: http://arxiv.org/abs/2205.01674

May 5, 2022, 1:10 a.m. | Shoukun Sun, Min Xian, Aleksandar Vakanski, Hossny Ghanem

cs.CV updates on arXiv.org arxiv.org

Robust self-training (RST) can augment the adversarial robustness of image
classification models without significantly sacrificing models'
generalizability. However, RST and other state-of-the-art defense approaches
failed to preserve the generalizability and reproduce their good adversarial
robustness on small medical image sets. In this work, we propose the
Multi-instance RST with a drop-max layer, namely MIRST-DM, which involves a
sequence of iteratively generated adversarial instances during training to
learn smoother decision boundaries on small datasets. The proposed drop-max
layer eliminates unstable features …

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