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Learning with noisy labels has aroused much research interest since dataannotations, especially for large-scale datasets, may be inevitably imperfect.Recent approaches resort to a semi-supervised learning problem by dividingtraining samples into clean and noisy sets. This paradigm, however, is prone tosignificant degeneration under heavy label noise, as the number of cleansamples is too small for conventional methods to behave well. In this paper, weintroduce a novel framework, termed as LC-Booster, to explicitly tacklelearning under extreme noise. The core idea of LC-Booster is to incorporatelabel correction into the sample selection, so that more purified samples,through the reliable label correction, can be utilized for training, therebyalleviating the confirmation bias. Experiments show that LC-Booster advancesstate-of-the-art results on several noisy-label benchmarks, including CIFAR-10,CIFAR-100, Clothing1M and WebVision. Remarkably, under the extreme 90\% noiseratio, LC-Booster achieves 92.9\% and 48.4\% accuracy on CIFAR-10 andCIFAR-100, surpassing state-of-the-art methods by a large margin.

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels