A formal proof shows no equivalence exists between adversarial risk and regularized risk in two-layer networks. Empirical results on Wide-ResNets confirm this impossibility persists in deeper, more expressive architectures.
Adversarial Training Equivalence Fails for Nonlinear Models
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