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We propose a novel framework for image clustering that incorporates jointrepresentation learning and clustering. Our method consists of two heads thatshare the same backbone network - a "representation learning" head and a"clustering" head. The "representation learning" head captures fine-grainedpatterns of objects at the instance level which serve as clues for the"clustering" head to extract coarse-grain information that separates objectsinto clusters. The whole model is trained in an end-to-end manner by minimizingthe weighted sum of two sample-oriented contrastive losses applied to theoutputs of the two heads. To ensure that the contrastive loss corresponding tothe "clustering" head is optimal, we introduce a novel critic function called"log-of-dot-product". Extensive experimental results demonstrate that ourmethod significantly outperforms state-of-the-art single-stage clusteringmethods across a variety of image datasets, improving over the best baseline byabout 5-7% in accuracy on CIFAR10/20, STL10, and ImageNet-Dogs. Further, the"two-stage" variant of our method also achieves better results than baselineson three challenging ImageNet subsets.

Clustering by Maximizing Mutual Information Across Views