Improving Adversarial Robustness via Promoting Ensemble Diversity

25 Jan 2019Tianyu PangKun XuChao DuNing ChenJun Zhu

Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble, e.g., by directly averaging the outputs, which ignores the interaction among networks... (read more)

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