Information-Theoretic Criteria for Knowledge Distillation in Multimodal Learning

2025-10-15

AI Summary

Cross-modal knowledge distillation (KD) often suffers from unstable performance due to the lack of theoretical foundations. To address this, we establish the first information-theoretic framework for cross-modal KD and propose the Cross-modal Complementarity Hypothesis (CCH): distillation is more effective when the mutual information between teacher and student representations exceeds that between the student and ground-truth labels. Leveraging a joint Gaussian model for theoretical analysis and practical mutual information estimation, we systematically validate CCH across diverse modalities—including image, text, video, audio, and multi-omics data. CCH provides an interpretable, generalizable criterion for teacher modality selection, significantly enhancing performance on weak modalities. It overcomes the empirical, hyperparameter-heavy nature of existing cross-modal KD methods and enables theory-driven, synergistic multimodal optimization.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

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: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The rapid increase in multimodal data availability has sparked significant interest in cross-modal knowledge distillation (KD) techniques, where richer "teacher" modalities transfer information to weaker "student" modalities during model training to improve performance. However, despite successes across various applications, cross-modal KD does not always result in improved outcomes, primarily due to a limited theoretical understanding that could inform practice. To address this gap, we introduce the Cross-modal Complementarity Hypothesis (CCH): we propose that cross-modal KD is effective when the mutual information between teacher and student representations exceeds the mutual information between the student representation and the labels. We theoretically validate the CCH in a joint Gaussian model and further confirm it empirically across diverse multimodal datasets, including image, text, video, audio, and cancer-related omics data. Our study establishes a novel theoretical framework for understanding cross-modal KD and offers practical guidelines based on the CCH criterion to select optimal teacher modalities for improving the performance of weaker modalities.

Problem

• Developing criteria for effective cross-modal knowledge distillation
• Addressing theoretical gaps in multimodal teacher-student learning
• Establishing when teacher modalities improve weaker student modalities

Innovation

• Introducing Cross-modal Complementarity Hypothesis for distillation
• Validating hypothesis with joint Gaussian model theoretically
• Applying criterion across multimodal datasets for performance improvement