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Multi-Label Prototype-Aware Structured Contrastive Distillation
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DOI：10.26599/TST.2024.9010182.png)
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摘要
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Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning. However, its direct application to multi-label learning proves challenging due to complex correlations in multi-label structures, causing student models to overlook more finely structured semantic relations present in the teacher model. In this paper, we present a solution called multi-label prototype-aware structured contrastive distillation, comprising two modules: Prototype-aware Contrastive Representation Distillation (PCRD) and prototype-aware cross-image structure distillation. The PCRD module maximizes the mutual information of prototype-aware representation between the student and teacher, ensuring semantic representation structure consistency to improve the compactness of intra-class and dispersion of inter-class representations. In the PCSD module, we introduce sample-to-sample and sample-to-prototype structured contrastive distillation to model prototype-aware cross-image structure consistency, guiding the student model to maintain a coherent label semantic structure with the teacher across multiple instances. To enhance prototype guidance stability, we introduce batch-wise dynamic prototype correction for updating class prototypes. Experimental results on three public benchmark datasets validate the effectiveness of our proposed method, demonstrating its superiority over state-of-the-art methods.
Keyword:
multi-label knowledge distillation
Prototype-aware Contrastive Representation Distillation (PCRD)
Prototype-aware Cross-image Structure Distillation (PCSD)
multi-label prototype learning
Prototype-aware Contrastive Representation Distillation (PCRD)
Prototype-aware Cross-image Structure Distillation (PCSD)
multi-label prototype learning
