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Contemporary deep clustering approaches often rely on either contrastive ornon-contrastive techniques to acquire effective representations for clusteringtasks. Contrastive methods leverage negative pairs to achieve homogenousrepresentations but can introduce class collision issues, potentiallycompromising clustering performance. On the contrary, non-contrastivetechniques prevent class collisions but may produce non-uniform representationsthat lead to clustering collapse. In this work, we propose a novel end-to-enddeep clustering approach named PIPCDR, designed to harness the strengths ofboth approaches while mitigating their limitations. PIPCDR incorporates apositive instance proximity loss and a cluster dispersion regularizer. Thepositive instance proximity loss ensures alignment between augmented views ofinstances and their sampled neighbors, enhancing within-cluster compactness byselecting genuinely positive pairs within the embedding space. Meanwhile, thecluster dispersion regularizer maximizes inter-cluster distances whileminimizing within-cluster compactness, promoting uniformity in the learnedrepresentations. PIPCDR excels in producing well-separated clusters, generatinguniform representations, avoiding class collision issues, and enhancingwithin-cluster compactness. We extensively validate the effectiveness of PIPCDRwithin an end-to-end Majorize-Minimization framework, demonstrating itscompetitive performance on moderate-scale clustering benchmark datasets andestablishing new state-of-the-art results on large-scale datasets.

Enhancing Clustering Representations with Positive Proximity and Cluster Dispersion Learning