  Unsupervised visual representation learning has gained much attention from
the computer vision community because of the recent achievement of contrastive
learning. Most of the existing contrastive learning frameworks adopt the
instance discrimination as the pretext task, which treating every single
instance as a different class. However, such method will inevitably cause class
collision problems, which hurts the quality of the learned representation.
Motivated by this observation, we introduced a weakly supervised contrastive
learning framework (WCL) to tackle this issue. Specifically, our proposed
framework is based on two projection heads, one of which will perform the
regular instance discrimination task. The other head will use a graph-based
method to explore similar samples and generate a weak label, then perform a
supervised contrastive learning task based on the weak label to pull the
similar images closer. We further introduced a K-Nearest Neighbor based
multi-crop strategy to expand the number of positive samples. Extensive
experimental results demonstrate WCL improves the quality of self-supervised
representations across different datasets. Notably, we get a new
state-of-the-art result for semi-supervised learning. With only 1\% and 10\%
labeled examples, WCL achieves 65\% and 72\% ImageNet Top-1 Accuracy using
ResNet50, which is even higher than SimCLRv2 with ResNet101.
