  Noisy labels damage the performance of deep networks. For robust learning, a
prominent two-stage pipeline alternates between eliminating possible incorrect
labels and semi-supervised training. However, discarding part of noisy labels
could result in a loss of information, especially when the corruption has a
dependency on data, e.g., class-dependent or instance-dependent. Moreover, from
the training dynamics of a representative two-stage method DivideMix, we
identify the domination of confirmation bias: pseudo-labels fail to correct a
considerable amount of noisy labels, and consequently, the errors accumulate.
To sufficiently exploit information from noisy labels and mitigate wrong
corrections, we propose Robust Label Refurbishment (Robust LR) a new hybrid
method that integrates pseudo-labeling and confidence estimation techniques to
refurbish noisy labels. We show that our method successfully alleviates the
damage of both label noise and confirmation bias. As a result, it achieves
state-of-the-art performance across datasets and noise types, namely CIFAR
under different levels of synthetic noise and Mini-WebVision and ANIMAL-10N
with real-world noise.
