  As the size of the dataset used in deep learning tasks increases, the noisy
label problem, which is a task of making deep learning robust to the
incorrectly labeled data, has become an important task. In this paper, we
propose a method of learning noisy label data using the label noise selection
with test-time augmentation (TTA) cross-entropy and classifier learning with
the NoiseMix method. In the label noise selection, we propose TTA cross-entropy
by measuring the cross-entropy to predict the test-time augmented training
data. In the classifier learning, we propose the NoiseMix method based on MixUp
and BalancedMix methods by mixing the samples from the noisy and the clean
label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset,
the proposed TTA cross-entropy outperformed the conventional cross-entropy and
the TTA uncertainty in detecting label noise data in the label noise selection
process. Moreover, the proposed NoiseMix not only outperformed the
state-of-the-art methods in the classification performance but also showed the
most robustness to the label noise in the classifier learning.
