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