Delving Deep into Semantic Relation Distillation

2025-03-27

AI Summary

Traditional knowledge distillation is limited to instance-level knowledge transfer, neglecting fine-grained semantic relationships among samples. To address this, we propose Semantic Relationship Distillation (SeRKD), the first framework that jointly leverages superpixel-driven semantic component extraction and vision transformer (ViT) token-level relational modeling. Specifically, SeRKD employs superpixel segmentation to identify local semantic structures, constructs a semantic relationship graph across samples, and introduces a relation-level KL divergence loss to enforce cross-sample semantic alignment. This approach fundamentally departs from conventional instance-level distillation paradigms. Extensive experiments on benchmarks including CIFAR-100 and ImageNet demonstrate that SeRKD consistently outperforms state-of-the-art methods. Notably, ViT-Tiny distilled via SeRKD achieves a 2.3% absolute accuracy gain, alongside improved generalization and robustness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Enhancing knowledge distillation via semantic relationships among samples
• Integrating superpixel-based semantics with relation-based distillation for model compression
• Improving Vision Transformers' performance through semantic relation distillation

Innovation

• Semantics-based Relation Knowledge Distillation (SeRKD)
• Leverages superpixels for semantic extraction
• Integrates relation-based distillation for model compression