Amogh Mahapatra

Enhancing Learning with Noisy Labels via Rockafellian Relaxation

Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling and weak labeling. Although neural networks (NNs) can tolerate modest amounts of these errors, their performance degrades substantially once the label error rate exceeds a certain threshold. We propose t…