most citedTheoretical Understanding of Learning from Adversarial Perturbations

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cs.LG2024★ 1 cited

Theoretical Understanding of Learning from Adversarial Perturbations

Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that a…