Skip to yearly menu bar Skip to main content


Poster
in
Workshop: 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning

Rethinking Robust Contrastive Learning from the Adversarial Perspective

Fatemeh Ghofrani · Mehdi Yaghouti · Pooyan Jamshidi

Keywords: [ representation analysis. ] [ Adversarial Robustness ] [ Contrastive Learning ] [ supervised contrastive learning ]


Abstract:

To advance the understanding of robust deep learning, we delve into the effects of adversarial training on self-supervised and supervised contrastive learning, alongside supervised learning. Our analysis uncovers significant disparities between adversarial and clean representations in standard-trained networks, across various learning algorithms. Remarkably, adversarial training mitigates these disparities and fosters the convergence of representations toward a universal set, regardless of the learning scheme used. Additionally, we observe that increasing the similarity between adversarial and clean representations, particularly near the end of the network, enhances network robustness. These findings offer valuable insights for designing and training effective and robust deep learning networks.

Chat is not available.