Discovering or Prescribing? Reflexive Structural Supervision for Contrastive Clustering

Self-supervised learning seeks to derive learning signals from unlabeled data itself, allowing meaningful representations to emerge without externally provided semantic supervision. For clustering, however, this principle leads to a fundamental tension. The organization of the data is precisely what the model is expected to discover, yet many contrastive clustering methods construct their supervision under an assumed clustering structure before that organization has been revealed. This tension is particularly relevant in unlabeled data, where multiple levels of organization may coexist and the appropriate granularity is generally unknown a priori. Committing representation learning to a single prescribed view of the data may therefore favor one possible organization while leaving other meaningful structural relationships under-explored. We develop Discovering Intrinsic Structure via Contrastive Clustering (DISC) as a reflexive learning framework, in which structural supervision is progressively inferred from the current representation space and fed back into representation learning. To avoid overlooking complementary structural information under a single prescribed view, DISC derives supervisory signals from discovered multi-granularity structural relationships. Furthermore, to reduce discrepancies between assignment results across granularities, DISC imposes cross-granularity consistency constraints between adjacent levels. Comparisons with recent alternatives demonstrate that DISC achieves strong representation quality and clustering performance without relying on a predefined clustering structure, while the ablation results consistently show the benefit of discovering and exploiting multi-granularity structure throughout learning. These findings suggest a shift in contrastive clustering from learning under a prescribed structure toward learning supervision through progressive structure discovery.