March 13, 2024, 4:44 a.m. | Meir Yossef Levi, Guy Gilboa

cs.LG updates on

arXiv:2308.05525v3 Announce Type: replace-cross
Abstract: The ability to cope with out-of-distribution (OOD) corruptions and adversarial attacks is crucial in real-world safety-demanding applications. In this study, we develop a general mechanism to increase neural network robustness based on focus analysis.
Recent studies have revealed the phenomenon of \textit{Overfocusing}, which leads to a performance drop. When the network is primarily influenced by small input regions, it becomes less robust and prone to misclassify under noise and corruptions.
However, quantifying overfocusing is still …

abstract adversarial adversarial attacks analysis applications arxiv attacks cloud cs.lg distribution focus general leads network networks neural network performance robustness safety studies study type world

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