Adversarial training is one of the most effective approaches defending
against adversarial examples for deep learning models. Unlike other defense
strategies, adversarial training aims to promote the robustness of models
intrinsically. During the last few years, adversarial training has been studied
and discussed from various aspects. A variety of improvements and developments
of adversarial training are proposed, which were, however, neglected in
existing surveys. For the first time in this survey, we systematically review
the recent progress on adversarial training for adversarial robustness with a
novel taxonomy. Then we discuss the generalization problems in adversarial
training from three perspectives. Finally, we highlight the challenges which
are not fully tackled and present potential future directions.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Bai_T/0/1/0/all/0/1">Tao Bai</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_J/0/1/0/all/0/1">Jinqi Luo</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_J/0/1/0/all/0/1">Jun Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Wen_B/0/1/0/all/0/1">Bihan Wen</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Q/0/1/0/all/0/1">Qian Wang</a>

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