English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Visual search, recommendation, and contrastive similarity learning powertechnologies that impact billions of users worldwide. Modern modelarchitectures can be complex and difficult to interpret, and there are severalcompeting techniques one can use to explain a search engine's behavior. We showthat the theory of fair credit assignment provides a $\textit{unique}$axiomatic solution that generalizes several existing recommendation- andmetric-explainability techniques in the literature. Using this formalism, weshow when existing approaches violate "fairness" and derive methods thatsidestep these shortcomings and naturally handle counterfactual information.More specifically, we show existing approaches implicitly approximatesecond-order Shapley-Taylor indices and extend CAM, GradCAM, LIME, SHAP, SBSM,and other methods to search engines. These extensions can extract pairwisecorrespondences between images from trained $\textit{opaque-box}$ models. Wealso introduce a fast kernel-based method for estimating Shapley-Taylor indicesthat require orders of magnitude fewer function evaluations to converge.Finally, we show that these game-theoretic measures yield more consistentexplanations for image similarity architectures.

Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning