Title: TE-VLM: Transfer Entropy for Vision Language Model Distillation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Transfer Entropy, Vision Language Model, Distillation

Abstract: Vision-Language Models (VLMs) have demonstrated impressive performance across various multimodal tasks. However, deploying large teacher models in real-world applications is often infeasible due to their high computational cost. To address this, knowledge distillation has been widely explored to transfer knowledge from a large teacher model to a smaller student model. In this paper, we propose a novel distillation framework that integrates Transfer Entropy (TE) as a regularization term to enhance information flow from the teacher to the student model. TE quantifies the directional dependency between teacher and student embeddings, encouraging the student model to effectively capture structural knowledge from the teacher. To efficiently approximate TE in high-dimensional embedding spaces, we introduce two surrogate formulations based on cosine similarity: (1) TE via cosine similarity of directional changes in embeddings and (2) TE via concatenated differences across modalities. Our experiments, conducted on the MSCOCO 2014 and Flickr8k datasets using CLIP-based teacher and student architectures, demonstrate that incorporating TE significantly improves retrieval performance. Through extensive analysis, we show that TE-based regularization enhances the student model's ability to capture multimodal associations and maintain representational consistency. Our findings suggest that TE is an effective tool for improving knowledge transfer in VLM distillation, bridging the performance gap between compact student models and their larger teacher counterparts.

TL;DR: Transfer Entropy for Vision Language Model Distillation