Decoder-free Robustness Disentanglement without (Additional) Supervision
- URL: http://arxiv.org/abs/2007.01356v1
- Date: Thu, 2 Jul 2020 19:51:40 GMT
- Title: Decoder-free Robustness Disentanglement without (Additional) Supervision
- Authors: Yifei Wang, Dan Peng, Furui Liu, Zhenguo Li, Zhitang Chen, Jiansheng
Yang
- Abstract summary: 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.
- Score: 42.066771710455754
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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