A new no-free-lunch construction and empirical scaling study argue that robust classification can require exponentially more data than standard classification, and that dataset size largely drives certified robust accuracy.
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Intriguing Properties of Robust Classification
A new no-free-lunch construction and empirical scaling study argue that robust classification can require exponentially more data than standard classification, and that dataset size largely drives certified robust accuracy.