  The success of learning with noisy labels (LNL) methods relies heavily on the
success of a warm-up stage where standard supervised training is performed
using the full (noisy) training set. In this paper, we identify a "warm-up
obstacle": the inability of standard warm-up stages to train high quality
feature extractors and avert memorization of noisy labels. We propose "Contrast
to Divide" (C2D), a simple framework that solves this problem by pre-training
the feature extractor in a self-supervised fashion. Using self-supervised
pre-training boosts the performance of existing LNL approaches by drastically
reducing the warm-up stage's susceptibility to noise level, shortening its
duration, and increasing extracted feature quality. C2D works out of the box
with existing methods and demonstrates markedly improved performance,
especially in the high noise regime, where we get a boost of more than 27% for
CIFAR-100 with 90% noise over the previous state of the art. In real-life noise
settings, C2D trained on mini-WebVision outperforms previous works both in
WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an
in-depth analysis of the framework, including investigating the performance of
different pre-training approaches and estimating the effective upper bound of
the LNL performance with semi-supervised learning. Code for reproducing our
experiments is available at https://github.com/ContrastToDivide/C2D
