Nils Körber

Drainage: A Unifying Framework for Addressing Class Uncertainty

Modern deep learning faces significant challenges with noisy labels, class ambiguity, as well as the need to robustly reject out-of-distribution or corrupted samples. In this work, we propose a unified framework based on the concept of a "drainage node" which we add at the output of the network. The