  In this paper, we address the problem of training deep neural networks in the
presence of severe label noise. Our proposed training algorithm ScanMix,
combines semantic clustering with semi-supervised learning (SSL) to improve the
feature representations and enable an accurate identification of noisy samples,
even in severe label noise scenarios. To be specific, ScanMix is designed based
on the expectation maximisation (EM) framework, where the E-step estimates the
value of a latent variable to cluster the training images based on their
appearance representations and classification results, and the M-step optimises
the SSL classification and learns effective feature representations via
semantic clustering. In our evaluations, we show state-of-the-art results on
standard benchmarks for symmetric, asymmetric and semantic label noise on
CIFAR-10 and CIFAR-100, as well as large scale real label noise on WebVision.
Most notably, for the benchmarks contaminated with large noise rates (80% and
above), our results are up to 27% better than the related work. The code is
available at https://github.com/ragavsachdeva/ScanMix.
