A joint training framework for learning with noisy labels
Sample selection and label correction are two effective strategies for learning with noisy labels(LNL).Both of these strategies have limitations for LNL tasks.Sample selection strategies usually ignore discriminative information in discarded samples,thereby degrading the performance of the algorithm.Label correction strategies commonly leverage self-labeling techniques,resulting in notorious error accumulation.In this paper,we propose a new learning framework that combines sample selection with label correction.Specifically,a novel selection criterion is designed to update the selected set online.Compared with the existing criterion,our proposal retains more boundary samples for the decision and can improve the generalization ability of the learning algorithm.For the label correction phase,we designed a joint label correction function.Compared with the conventional self-label strategy,a multiview label correction is proposed to combine multiple feature space views,which can alleviate the effect of error accumulation.In addition,we propose a contrastive learning regularization term to enhance the learning of modified label quality and model representation from the feature perspective.Our framework achieves the new state of the art on four challenging benchmarks,demonstrating its great effectiveness in LNL.
learning with noisy labelssample selectionlabel correctioncontrastive learning
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