  For an image query, unsupervised contrastive learning labels crops of the
same image as positives, and other image crops as negatives. Although
intuitive, such a native label assignment strategy cannot reveal the underlying
semantic similarity between a query and its positives and negatives, and
impairs performance, since some negatives are semantically similar to the query
or even share the same semantic class as the query. In this work, we first
prove that for contrastive learning, inaccurate label assignment heavily
impairs its generalization for semantic instance discrimination, while accurate
labels benefit its generalization. Inspired by this theory, we propose a novel
self-labeling refinement approach for contrastive learning. It improves the
label quality via two complementary modules: (i) self-labeling refinery (SLR)
to generate accurate labels and (ii) momentum mixup (MM) to enhance similarity
between query and its positive. SLR uses a positive of a query to estimate
semantic similarity between a query and its positive and negatives, and
combines estimated similarity with vanilla label assignment in contrastive
learning to iteratively generate more accurate and informative soft labels. We
theoretically show that our SLR can exactly recover the true semantic labels of
label-corrupted data, and supervises networks to achieve zero prediction error
on classification tasks. MM randomly combines queries and positives to increase
semantic similarity between the generated virtual queries and their positives
so as to improves label accuracy. Experimental results on CIFAR10, ImageNet,
VOC and COCO show the effectiveness of our method. PyTorch code and model will
be released online.
