Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning

no code implementations • 1 Jan 2023 • Fengbei Liu, Yuanhong Chen, Chong Wang, Yu Tain, Gustavo Carneiro

Also, the new sample selection is based on multi-view consensus, which uses the label views from training labels and model predictions to divide the training set into clean and noisy for training the multi-class model and to re-label the training samples with multiple top-ranked labels for training the multi-label model.

Learning with noisy labels Multi-Label Learning