Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
Usha Bhalla Alex Oesterling Suraj Srinivas Flavio P. Calmon Himabindu Lakkaraju
CLIP embeddings have demonstrated remarkable performance across a wide range
of computer vision tasks. However, these high-dimensional, dense vector
representations are not easily interpretable, restricting their usefulness in
downstream applications that require transparency. In this work, we empirically
show that CLIP's latent space is highly structured, and consequently that CLIP
representations can be decomposed into their underlying semantic components. We
leverage this understanding to propose a novel method, Sparse Linear Concept
Embeddings (SpLiCE), for transforming CLIP representations into sparse linear
combinations of human-interpretable concepts. Distinct from previous work,
SpLiCE does not require concept labels and can be applied post hoc. Through
extensive experimentation with multiple real-world datasets, we validate that
the representations output by SpLiCE can explain and even replace traditional
dense CLIP representations, maintaining equivalent downstream performance while
significantly improving their interpretability. We also demonstrate several use
cases of SpLiCE representations including detecting spurious correlations,
model editing, and quantifying semantic shifts in datasets.