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