Enhancing Clustering Representations with Positive Proximity and Cluster Dispersion Learning. (arXiv:2311.00731v1 [cs.LG])

Contemporary deep clustering approaches often rely on either contrastive or
non-contrastive techniques to acquire effective representations for clustering
tasks. Contrastive methods leverage negative pairs to achieve homogenous
representations but can introduce class collision issues, potentially
compromising clustering performance. On the contrary, non-contrastive
techniques prevent class collisions but may produce non-uniform representations
that lead to clustering collapse. In this work, we propose a novel end-to-end
deep clustering approach named PIPCDR, designed to harness the strengths of
both approaches while mitigating their …

arxiv cluster clustering collision negative performance positive tasks