  Noisy labels are ubiquitous in real-world datasets, especially in the
large-scale ones derived from crowdsourcing and web searching. It is
challenging to train deep neural networks with noisy datasets since the
networks are prone to overfitting the noisy labels during training, resulting
in poor generalization performance. During an early learning phase, deep neural
networks have been observed to fit the clean samples before memorizing the
mislabeled samples. In this paper, we dig deeper into the representation
distributions in the early learning phase and find that, regardless of their
noisy labels, learned representations of images from the same category still
congregate together. Inspired by it, we propose a framework that trains the
model with new synthetic samples to mitigate the impact of noisy labels.
Specifically, we propose a mixing strategy to create the synthetic samples by
aggregating original samples with their top-K nearest neighbours, wherein the
weights are calculated using a mixture model learning from the per-sample loss
distribution. To enhance the performance in the presence of extreme label
noise, we estimate the soft targets by gradually correcting the noisy labels.
Furthermore, we demonstrate that the estimated soft targets yield a more
accurate approximation to ground truth labels and the proposed method produces
a superior quality of learned representations with more separated and clearly
bounded clusters. The extensive experiments in two benchmarks (CIFAR-10 and
CIFAR-100) and two larg-scale real-world datasets (Clothing1M and Webvision)
demonstrate that our approach outperforms the state-of-the-art methods and
robustness of the learned representation.
