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 this https URL

本文提出了一种新颖的端到端深度聚类方法，采用了扩散采样和硬度感知自蒸馏策略（HaDis），通过扩散采样对实例进行对齐以提高簇内紧密度，引入硬度感知自蒸馏机制来提高样本权重的自适应调整能力，同时结合原型对比学习来增强簇间可分性和簇内紧密度，实验证明了 HaDis 方法在五个挑战性图像数据集上相比现有技术具有更优的聚类性能。