Speaker
Mathieu Serrurier
(IRIT Toulouse)
Description
The lack of robustness and explainability in neural networks is directly linked to the arbitrarily high Lipschitz constant of deep models. Although constraining the Lipschitz constant has been shown to improve these properties, it can make it challenging to learn with classical loss functions. In this presentation, we explain how to control this constant, and demonstrate that training such networks requires defining specific loss functions and optimization processes. To this end, we propose a loss function based on optimal transport that not only certifies robustness but also converts adversarial examples into provable counterfactual examples.
