Decoding class dynamics in learning with noisy labels

Albert Tatjer, Bhalaji Nagarajan, Ricardo Marques, Petia Radeva

Abstract: Highlights•Label noise leads to reduced generalization in deep learning models.•Global Noise Conundrum exists in several Learning with Noisy Labels sample-selection methods.•Class-Conditional Local noise Model (CCLM) uses per-class-based local distribution of samples with local thresholds.•Class-aware decision boundary of CCLM leads to a better clean-noise split.•Locally adapted clean-noise split yielded improvements in both real and synthetic noise benchmarks.