The paper "Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE 📝)" by Usha Bhalla addresses the challenge of interpreting high-dimensional, dense vector representations from CLIP, a state-of-the-art model in computer vision. The authors propose SpLiCE, a novel method that transforms CLIP representations into sparse linear combinations of human-interpretable concepts. This approach leverages the structured nature of CLIP's latent space to decompose its representations into semantic components, enhancing interpretability without sacrificing downstream performance. Key contributions of the paper include: 1. **Formalizing the Feasibility of Decomposition**: The authors derive conditions under which CLIP representations can be decomposed into sparse semantic representations, based on assumptions about the data-generating process and the properties of CLIP encoders. 2. **Introduction of SpLiCE**: SpLiCE is designed to decompose dense CLIP embeddings into sparse, nonnegative linear combinations of a concept dictionary, using dictionary learning techniques. 3. **Experimental Validation**: Extensive experiments on multiple real-world datasets demonstrate that SpLiCE can recover interpretable representations with minimal loss in performance, while accurately capturing the semantics of the underlying inputs. 4. **Use Cases**: The paper presents three case studies: spurious correlation detection, model editing, and quantifying distribution shifts in datasets, showcasing the practical applications of SpLiCE. The authors conclude that SpLiCE provides a valuable tool for improving the interpretability of CLIP's representations, making them more useful in downstream applications that require transparency and interpretability.The paper "Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE 📝)" by Usha Bhalla addresses the challenge of interpreting high-dimensional, dense vector representations from CLIP, a state-of-the-art model in computer vision. The authors propose SpLiCE, a novel method that transforms CLIP representations into sparse linear combinations of human-interpretable concepts. This approach leverages the structured nature of CLIP's latent space to decompose its representations into semantic components, enhancing interpretability without sacrificing downstream performance. Key contributions of the paper include: 1. **Formalizing the Feasibility of Decomposition**: The authors derive conditions under which CLIP representations can be decomposed into sparse semantic representations, based on assumptions about the data-generating process and the properties of CLIP encoders. 2. **Introduction of SpLiCE**: SpLiCE is designed to decompose dense CLIP embeddings into sparse, nonnegative linear combinations of a concept dictionary, using dictionary learning techniques. 3. **Experimental Validation**: Extensive experiments on multiple real-world datasets demonstrate that SpLiCE can recover interpretable representations with minimal loss in performance, while accurately capturing the semantics of the underlying inputs. 4. **Use Cases**: The paper presents three case studies: spurious correlation detection, model editing, and quantifying distribution shifts in datasets, showcasing the practical applications of SpLiCE. The authors conclude that SpLiCE provides a valuable tool for improving the interpretability of CLIP's representations, making them more useful in downstream applications that require transparency and interpretability.