  Recently, many unsupervised deep learning methods have been proposed to learn
clustering with unlabelled data. By introducing data augmentation, most of the
latest methods look into deep clustering from the perspective that the original
image and its transformation should share similar semantic clustering
assignment. However, the representation features could be quite different even
they are assigned to the same cluster since softmax function is only sensitive
to the maximum value. This may result in high intra-class diversities in the
representation feature space, which will lead to unstable local optimal and
thus harm the clustering performance. To address this drawback, we proposed
Deep Robust Clustering (DRC). Different from existing methods, DRC looks into
deep clustering from two perspectives of both semantic clustering assignment
and representation feature, which can increase inter-class diversities and
decrease intra-class diversities simultaneously. Furthermore, we summarized a
general framework that can turn any maximizing mutual information into
minimizing contrastive loss by investigating the internal relationship between
mutual information and contrastive learning. And we successfully applied it in
DRC to learn invariant features and robust clusters. Extensive experiments on
six widely-adopted deep clustering benchmarks demonstrate the superiority of
DRC in both stability and accuracy. e.g., attaining 71.6% mean accuracy on
CIFAR-10, which is 7.1% higher than state-of-the-art results.
