Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevitably leads to severe accuracy reduction as it discards the non-robust yet useful features. This motivates us to preserve both robust and non-robust features and separate them with disentangled representation learning. Our proposed Adversarial Asymmetric Training (AAT) algorithm can reliably disentangle robust and non-robust representations without additional supervision on robustness. Empirical results show our method does not only successfully preserve accuracy by combining two representations, but also achieve much better disentanglement than previous work.
@article{arxiv.2007.01356,
title = {Decoder-free Robustness Disentanglement without (Additional) Supervision},
author = {Yifei Wang and Dan Peng and Furui Liu and Zhenguo Li and Zhitang Chen and Jiansheng Yang},
journal= {arXiv preprint arXiv:2007.01356},
year = {2020}
}