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We consider the problem of training robust and accurate deep neural networks(DNNs) when subject to various proportions of noisy labels. Large-scaledatasets tend to contain mislabeled samples that can be memorized by DNNs,impeding the performance. With appropriate handling, this degradation can bealleviated. There are two problems to consider: how to distinguish cleansamples and how to deal with noisy samples. In this paper, we present EnsembleNoise-robust K-fold Cross-Validation Selection (E-NKCVS) to effectively selectclean samples from noisy data, solving the first problem. For the secondproblem, we create a new pseudo label for any sample determined to have anuncertain or likely corrupt label. E-NKCVS obtains multiple predicted labelsfor each sample and the entropy of these labels is used to tune the weightgiven to the pseudo label and the given label. Theoretical analysis andextensive verification of the algorithms in the noisy label setting areprovided. We evaluate our approach on various image and text classificationtasks where the labels have been manually corrupted with different noiseratios. Additionally, two large real-world noisy datasets are also used,Clothing-1M and WebVision. E-NKCVS is empirically shown to be highly tolerantto considerable proportions of label noise and has a consistent improvementover state-of-the-art methods. Especially on more difficult datasets withhigher noise ratios, we can achieve a significant improvement over thesecond-best model. Moreover, our proposed approach can easily be integratedinto existing DNN methods to improve their robustness against label noise.

An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels