Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation
This work addresses the problem of validating and comparing concept interpretation methods for deep learning models like CLIP, offering a statistically robust approach that mitigates spurious cues.
The paper tackles the lack of statistical rigor in concept-based interpretation of CLIP embeddings by introducing a hypothesis testing framework and post-hoc decomposition method, resulting in a 22.6% increase in worst-group accuracy on a spurious correlation dataset.
Concept-based approaches, which aim to identify human-understandable concepts within a model's internal representations, are a promising method for interpreting embeddings from deep neural network models, such as CLIP. While these approaches help explain model behavior, current methods lack statistical rigor, making it challenging to validate identified concepts and compare different techniques. To address this challenge, we introduce a hypothesis testing framework that quantifies rotation-sensitive structures within the CLIP embedding space. Once such structures are identified, we propose a post-hoc concept decomposition method. Unlike existing approaches, it offers theoretical guarantees that discovered concepts represent robust, reproducible patterns (rather than method-specific artifacts) and outperforms other techniques in terms of reconstruction error. Empirically, we demonstrate that our concept-based decomposition algorithm effectively balances reconstruction accuracy with concept interpretability and helps mitigate spurious cues in data. Applied to a popular spurious correlation dataset, our method yields a 22.6% increase in worst-group accuracy after removing spurious background concepts.