Summary of Interpreting Clip with Sparse Linear Concept Embeddings (splice), by Usha Bhalla et al.
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
by Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flavio P. Calmon, Himabindu Lakkaraju
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel method, Sparse Linear Concept Embeddings (SpLiCE), to provide interpretability for CLIP embeddings in multimodal applications. CLIP’s high-dimensional vectors are not easily understandable, limiting their use in downstream tasks that require transparency. SpLiCE leverages the semantic structure of CLIP’s latent space to decompose representations into human-interpretable concepts. Unlike previous work, SpLiCE is task-agnostic and can be used without training to explain and replace traditional dense CLIP representations, maintaining high performance while improving interpretability. The paper demonstrates significant use cases of SpLiCE, including detecting spurious correlations and model editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it easier to understand how a special kind of computer representation works. This representation is called CLIP, and it’s good at recognizing pictures and words. But right now, it’s hard to figure out what the representation actually means. The researchers created a new method that can break down this representation into smaller parts that make sense to humans. This new method is called SpLiCE. It allows us to understand what’s going on in these representations without having to train them again. This is useful because it helps us detect mistakes and fix problems with the models. |
Keywords
* Artificial intelligence * Latent space
