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Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
March 1, 2024, 5:42 a.m. | Qiao Han, yong huang, xinling Guo, Yiteng Zhai, Yu Qin, Yao Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate this deficiency, we propose a novel adversarial defense method called "Immunity" (Innovative MoE with MUtual information \& positioN stabilITY) based on a modified Mixture-of-Experts (MoE) architecture in this work. The key enhancements to the standard MoE are two-fold: 1) integrating of Random Switch Gates (RSGs) to obtain diverse network structures …
abstract adversarial adversarial examples arxiv cs.cr cs.lg defense examples experts information making moe networks neural networks novel predictions stability studies type vulnerability
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