Decoder-free Robustness Disentanglement without (Additional) Supervision

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.

References in corpus (5)

• Theoretically Principled Trade-off between Robustness and Accuracy
• Deep Convolutional Inverse Graphics Network
• Disentangling factors of variation in deep representations using adversarial training
• On Detecting Adversarial Perturbations
• Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples