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Deep neural networks are prone to overfitting noisy labels, resulting in poorgeneralization performance. To overcome this problem, we present a simple andeffective method self-ensemble label correction (SELC) to progressively correctnoisy labels and refine the model. We look deeper into the memorizationbehavior in training with noisy labels and observe that the network outputs arereliable in the early stage. To retain this reliable knowledge, SELC usesensemble predictions formed by an exponential moving average of network outputsto update the original noisy labels. We show that training with SELC refinesthe model by gradually reducing supervision from noisy labels and increasingsupervision from ensemble predictions. Despite its simplicity, compared withmany state-of-the-art methods, SELC obtains more promising and stable resultsin the presence of class-conditional, instance-dependent, and real-world labelnoise. The code is available at https://github.com/MacLLL/SELC.

SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels