# Recent Advances in Adversarial Training for Adversarial Robustness Source: [Recent Advances in Adversarial Training for Adversarial Robustness]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:07:38.365Z Authors: Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang Published: 2021-02-02T07:10:22.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet (test val) (Standard and Robust Accuracy)](): Image Classification; ImageNet (test val); Standard Accuracy: 0.6738. ## Original sources and implementation [Original paper]() [PDF]()