B. Braeckeveldt

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

Evidential Adversarial Training (EV-AT), which models uncertainty through a Dirichlet distribution and combines an evidence-based loss promoting clean accuracy and reliable uncertainty with a robust evidence-alignment loss matching clean and adversarial predictions in log Dirichlet-parameter space, is proposed.