CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
This article introduces the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU), aimed at improving machine learning performance on noisy labeled datasets. By utilizing data alignment rather than relying on clean labels, the method demonstrates robust results across various datasets, outperforming existing approaches.