Delving Deep into Semantic Relation Distillation
This addresses model compression for lightweight deployment in vision tasks, particularly for Vision Transformers, but appears incremental as it builds on existing distillation frameworks.
The paper tackles the problem of traditional knowledge distillation failing to capture semantic relationships by introducing SeRKD, a semantics-relation distillation method that uses superpixels to transfer knowledge more comprehensively. Experimental results on benchmark datasets show SeRKD outperforms existing methods in enhancing model performance and generalization.
Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional distillation approaches focus on transferring knowledge at the instance level, but fail to capture nuanced semantic relationships within the data. In response, this paper introduces a novel methodology, Semantics-based Relation Knowledge Distillation (SeRKD), which reimagines knowledge distillation through a semantics-relation lens among each sample. By leveraging semantic components, \ie, superpixels, SeRKD enables a more comprehensive and context-aware transfer of knowledge, which skillfully integrates superpixel-based semantic extraction with relation-based knowledge distillation for a sophisticated model compression and distillation. Particularly, the proposed method is naturally relevant in the domain of Vision Transformers (ViTs), where visual tokens serve as fundamental units of representation. Experimental evaluations on benchmark datasets demonstrate the superiority of SeRKD over existing methods, underscoring its efficacy in enhancing model performance and generalization capabilities.