Improving adversarial robustness by learning shared information

摘要

•Inspired by multi-view representation learning, we propose a scheme casting adversarial examples as a secondary view.•We propose and analyze our loss for learning representations with shared information between clean and adversarial samples.•We demonstrate that our method achieves improved robust vs. natural accuracy tradeoffs over several attacks and datasets.