  Recent advancements in self-supervised learning have reduced the gap between
supervised and unsupervised representation learning. However, most
self-supervised and deep clustering techniques rely heavily on data
augmentation, rendering them ineffective for many learning tasks where
insufficient domain knowledge exists for performing augmentation. We propose a
new self-distillation based algorithm for domain-agnostic clustering. Our
method builds upon the existing deep clustering frameworks and requires no
separate student model. The proposed method outperforms existing domain
agnostic (augmentation-free) algorithms on CIFAR-10. We empirically demonstrate
that knowledge distillation can improve unsupervised representation learning by
extracting richer `dark knowledge' from the model than using predicted labels
alone. Preliminary experiments also suggest that self-distillation improves the
convergence of DeepCluster-v2.
