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Learning deep neural network (DNN) classifier with noisy labels is achallenging task because the DNN can easily over-fit on these noisy labels dueto its high capability. In this paper, we present a very simple but effectivetraining paradigm called P-DIFF, which can train DNN classifiers but obviouslyalleviate the adverse impact of noisy labels. Our proposed probabilitydifference distribution implicitly reflects the probability of a trainingsample to be clean, then this probability is employed to re-weight thecorresponding sample during the training process. P-DIFF can also achieve goodperformance even without prior knowledge on the noise rate of training samples.Experiments on benchmark datasets also demonstrate that P-DIFF is superior tothe state-of-the-art sample selection methods.

P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions