Representation Learning for Clustering via Building Consensus

In this paper, we focus on deep clustering and unsupervised representation learning for images. Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be closer in the representation space (exemplar consistency), and/or similar images have a similar cluster assignment (population consistency). We define an additional notion of consistency, consensus consistency, which ensures that representations are learnt to induce similar partitions for variations in the representation space, different clustering algorithms or different initializations of a clustering algorithm. We define a clustering loss by performing variations in the representation space and seamlessly integrate all three consistencies (consensus, exemplar and population) into an end-to-end learning framework. The proposed algorithm, Consensus Clustering using Unsupervised Representation Learning (ConCURL) improves the clustering performance over state-of-the art methods on four out of five image datasets. Further, we extend the evaluation procedure for clustering to reflect the challenges in real world clustering tasks, such as clustering performance in the case of distribution shift. We also perform a detailed ablation study for a deeper understanding of the algorithm.