  Learning from noisy data is a challenging task that significantly degenerates
the model performance. In this paper, we present TCL, a novel twin contrastive
learning model to learn robust representations and handle noisy labels for
classification. Specifically, we construct a Gaussian mixture model (GMM) over
the representations by injecting the supervised model predictions into GMM to
link label-free latent variables in GMM with label-noisy annotations. Then, TCL
detects the examples with wrong labels as the out-of-distribution examples by
another two-component GMM, taking into account the data distribution. We
further propose a cross-supervision with an entropy regularization loss that
bootstraps the true targets from model predictions to handle the noisy labels.
As a result, TCL can learn discriminative representations aligned with
estimated labels through mixup and contrastive learning. Extensive experimental
results on several standard benchmarks and real-world datasets demonstrate the
superior performance of TCL. In particular, TCL achieves 7.5\% improvements on
CIFAR-10 with 90\% noisy label -- an extremely noisy scenario. The source code
is available at \url{https://github.com/Hzzone/TCL}.
