  Unsupervised representation learning methods like SwAV are proved to be
effective in learning visual semantics of a target dataset. The main idea
behind these methods is that different views of a same image represent the same
semantics. In this paper, we further introduce an add-on module to facilitate
the injection of the knowledge accounting for spatial cross correlations among
the samples. This in turn results in distilling intra-class information
including feature level locations and cross similarities between same-class
instances. The proposed add-on can be added to existing methods such as the
SwAV. We can later remove the add-on module for inference without any
modification of the learned weights. Through an extensive set of empirical
evaluations, we verify that our method yields an improved performance in
detecting the class activation maps, top-1 classification accuracy, and
down-stream tasks such as object detection, with different configuration
settings.
