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Gradient Methods Provably Converge to Non-Robust Networks

Gal Vardi · Gilad Yehudai · Ohad Shamir

Hall J (level 1) #723

Keywords: [ robustness ] [ implicit bias ] [ Deep Learning Theory ]

Abstract: Despite a great deal of research, it is still unclear why neural networks are so susceptible to adversarial examples. In this work, we identify natural settings where depth-$2$ ReLU networks trained with gradient flow are provably non-robust (susceptible to small adversarial $\ell_2$-perturbations), even when robust networks that classify the training dataset correctly exist.Perhaps surprisingly, we show that the well-known implicit bias towards margin maximization induces bias towards non-robust networks, by proving that every network which satisfies the KKT conditions of the max-margin problem is non-robust.

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