Deep Clustering with Diffused Sampling and Hardness-aware
  Self-distillation
Hai-Xin Zhang Dong Huang
Deep clustering has gained significant attention due to its capability in
learning clustering-friendly representations without labeled data. However,
previous deep clustering methods tend to treat all samples equally, which
neglect the variance in the latent distribution and the varying difficulty in
classifying or clustering different samples. To address this, this paper
proposes a novel end-to-end deep clustering method with diffused sampling and
hardness-aware self-distillation (HaDis). Specifically, we first align one view
of instances with another view via diffused sampling alignment (DSA), which
helps improve the intra-cluster compactness. To alleviate the sampling bias, we
present the hardness-aware self-distillation (HSD) mechanism to mine the
hardest positive and negative samples and adaptively adjust their weights in a
self-distillation fashion, which is able to deal with the potential imbalance
in sample contributions during optimization. Further, the prototypical
contrastive learning is incorporated to simultaneously enhance the
inter-cluster separability and intra-cluster compactness. Experimental results
on five challenging image datasets demonstrate the superior clustering
performance of our HaDis method over the state-of-the-art. Source code is
available at https://github.com/Regan-Zhang/HaDis.