Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models

2026-05-21 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

CEDAR (Conceptual Embedding Disentanglement via Adaptive Rotation) is a new post-hoc method designed to reveal the compositional structure within pretrained vision-language model embeddings. It addresses the opacity of these embeddings and the limitations of sparse autoencoders (SAEs), which typically expand representation dimensions, thereby compromising original geometry and introducing redundancy. CEDAR learns an invertible transformation with a top-k sparsity bottleneck, concentrating semantic information into axis-aligned disentangled coordinates without increasing dimensionality. This allows individual coordinates to be interpreted with textual concepts in CLIP-like architectures or decoded into natural language descriptions for generative models such as BLIP. Experiments show CEDAR achieves a competitive reconstruction-sparsity trade-off, yielding explanations that are more interpretable and better aligned with human perception, suggesting entanglement can be resolved through a suitable change of basis.

Key takeaway

For AI Scientists and Research Scientists working with vision-language models, CEDAR offers a crucial method to enhance interpretability without the computational overhead of dimensionality expansion. You should consider integrating CEDAR to gain deeper insights into your model's internal semantics, particularly when debugging or refining multimodal representations, as it provides more human-aligned explanations.

Key insights

CEDAR disentangles vision-language model embeddings post-hoc, revealing compositional structure without increasing dimensionality, improving interpretability.

Principles

• Apparent entanglement in VLM representations is resolvable via basis change.
• Disentanglement can occur post-hoc without dimensionality expansion.
• Top-k sparsity bottleneck concentrates semantic information.

Method

CEDAR learns an invertible transformation with a top-k sparsity bottleneck on pretrained embeddings. It concentrates semantic information into axis-aligned disentangled coordinates, enabling interpretation without dimensionality increase.

In practice

• Interpret individual CLIP embedding coordinates with text.
• Decode BLIP embedding coordinates into natural language.

Topics

• Vision-Language Models
• Embedding Disentanglement
• Model Interpretability
• Sparse Representations
• CLIP
• BLIP

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.