Hide and Seek: HIDE Concepts to SEEK explanations for black box models
[Widzenie komputerowe] Autorzy: Bhanu Tokas, Rahul Nair, Rockwell Jackson i 1 więcej
Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods interpret models using human-understandable concepts. Existing concept-based methods can only interpret classification and regression models. They require access to model weights or large concept-annotated datasets, and most methods cannot provide image-level attribution. We propose Hide and Seek (HnS), a new method for concept-level attribution that does not require access to model weights or labeled concept datasets. HnS can interpret a wide range of vision models. Given a concept and image set, HnS retrieves concept-aligned images using a vision-language model, localizes the concept within each image, masks the localized region, and measures attribution from the change in the black-box model's output between the original and masked images…
- Kategoria arxiv
- cs.CV
- Kategoria
- Widzenie komputerowe