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Algorithms which minimize the averaged loss have been widely designed fordealing with noisy labels. Intuitively, when there is a finite training sample,penalizing the variance of losses will improve the stability and generalizationof the algorithms. Interestingly, we found that the variance should beincreased for the problem of learning with noisy labels. Specifically,increasing the variance will boost the memorization effects and reduce theharmfulness of incorrect labels. By exploiting the label noise transitionmatrix, regularizers can be easily designed to reduce the variance of lossesand be plugged in many existing algorithms. Empirically, the proposed method byincreasing the variance of losses significantly improves the generalizationability of baselines on both synthetic and real-world datasets.

Do We Need to Penalize Variance of Losses for Learning with Label Noise?