Model-Agnostic Explainability for Visual Search.
Year: 2021
Type: preprint
Abstract: What makes two images similar? We propose new approaches to generate model-agnostic explanations for image similarity, search, and retrieval. In particular, we extend Class Activation Maps (CAMs), Additive Shapley Explanations (SHAP), and Locally Interpretable Model-Agnostic Explanations (LIME) to the domain of image retrieval and search. These approaches enable black and grey-box model introspection and can help diagnose errors and understand the rationale behind a model's similarity judgments. Furthermore, we extend these approaches to extract a full pairwise correspondence between the query and retrieved image pixels, an approach we call interpretations. Formally, we show joint search interpretations arise from projecting Harsanyi dividends, and that this approach generalizes Shapley Values and The Shapley-Taylor indices. We introduce a fast kernel-based method for estimating Shapley-Taylor indices and empirically show that these game-theoretic measures yield more consistent explanations for image similarity architectures.
Source: arXiv (Cornell University)
Authors: Mark Hamilton, Scott Lundberg, Lei Zhang, Stephanie Fu, William T. Freeman
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3134584755