Reliability-Disentangled Distillation Hashing: Selective Bit and Semantic Purification for Multimodal Retrieval

Unsupervised multimodal hashing is critical for retrieval efficiency, yet suffers from excessive parameters and redundant code bits. Standard distillation methods treat the teacher model as a perfect supervisor, forcing the student model to mimic representations that inherently contain stochastic noise and errors. To break this, we propose a Reliability-Disentangled Distillation Hashing (RDDH) framework for multimodal retrieval, which energizes the student to selectively inherit reliable knowledge via a dual-granularity purification mechanism. At the micro level (bit-wise), we present an information-theoretic reliability module based on secondary clustering that explicitly disentangles discriminative bits from redundant noise via entropy and polarity analysis, focusing the student on semantically stable directions. At the macro level (semantic-wise), we rectify the geometry of teacher through a filtered bidirectional contrastive objective that acts as a gatekeeper, masking the false-positive alignment and false-negative repulsion to preserve the intrinsic topological structure. Specifically, to balance the supervision sparsity from strict filtering, an intra-student contrastive task is integrated to ensure global data utilization. Extensive experiments on three benchmark datasets demonstrate that RDDH effectively purifies the distilled knowledge, realizing a lightweight student to significantly surpass the performance of teacher.