DRP: Long-Tailed Deep Clustering via Denoising-Supported Repulsive Dirichlet Process

In long-tailed deep clustering, dense head regions tend to occupy redundant prototype capacity, leading to head-class over-segmentation and tail-class absorption. To address this issue, we propose DRP, a nonparametric prototype inference framework for long-tailed deep clustering that rectifies prototype-capacity allocation bias by governing prototype birth, activation, repulsion and reallocation. DRP builds a semantic support measure via denoising consistency to stop dense regions from gaining unfair prototype birth advantages. Using this measure as the base distribution, it constructs a Dirichlet-process reference law and derives a Gibbs-tilted prototype prior minimizing KL divergence under a log-determinantal redundancy constraint. This prior controls active prototype count and suppresses redundant similar prototypes in head regions. For under-covered but stable semantic regions, cardinality-preserving prototype exchange reallocates redundant prototype slots. An identity-initialized residual adapter injects the corrected prototype structure back into the representation space; prototype-balanced feedback and conservative regularization limit representation drift. All components are integrated into a finite alternating MAP inference pipeline requiring no ground-truth labels, class frequencies or predefined class number. Extensive experiments validate DRP’s state-of-the-art performance on long-tailed clustering benchmarks, strong generalization to balanced data, and ability to infer unknown class counts.