  Learning with noisy labels has aroused much research interest since data
annotations, especially for large-scale datasets, may be inevitably imperfect.
Recent approaches resort to a semi-supervised learning problem by dividing
training samples into clean and noisy sets. This paradigm, however, is prone to
significant degeneration under heavy label noise, as the number of clean
samples is too small for conventional methods to behave well. In this paper, we
introduce a novel framework, termed as LC-Booster, to explicitly tackle
learning under extreme noise. The core idea of LC-Booster is to incorporate
label correction into the sample selection, so that more purified samples,
through the reliable label correction, can be utilized for training, thereby
alleviating the confirmation bias. Experiments show that LC-Booster advances
state-of-the-art results on several noisy-label benchmarks, including CIFAR-10,
CIFAR-100, Clothing1M and WebVision. Remarkably, under the extreme 90\% noise
ratio, LC-Booster achieves 92.9\% and 48.4\% accuracy on CIFAR-10 and
CIFAR-100, surpassing state-of-the-art methods by a large margin.
