  Labels are costly and sometimes unreliable. Noisy label learning,
semi-supervised learning, and contrastive learning are three different
strategies for designing learning processes requiring less annotation cost.
Semi-supervised learning and contrastive learning have been recently
demonstrated to improve learning strategies that address datasets with noisy
labels. Still, the inner connections between these fields as well as the
potential to combine their strengths together have only started to emerge. In
this paper, we explore further ways and advantages to fuse them. Specifically,
we propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and
CoDiM (Contrastive DivideMix), a novel algorithm for learning with noisy
labels. CSSL leverages the power of classical semi-supervised learning and
contrastive learning technologies and is further adapted to CoDiM, which learns
robustly from multiple types and levels of label noise. We show that CoDiM
brings consistent improvements and achieves state-of-the-art results on
multiple benchmarks.

Machine Learning;Computer Vision and Pattern Recognition