  Transformers have become a default architecture in computer vision, but
understanding what drives their predictions remains a challenging problem.
Current explanation approaches rely on attention values or input gradients, but
these provide a limited view of a model's dependencies. Shapley values offer a
theoretically sound alternative, but their computational cost makes them
impractical for large, high-dimensional models. In this work, we aim to make
Shapley values practical for vision transformers (ViTs). To do so, we first
leverage an attention masking approach to evaluate ViTs with partial
information, and we then develop a procedure to generate Shapley value
explanations via a separate, learned explainer model. Our experiments compare
Shapley values to many baseline methods (e.g., attention rollout, GradCAM,
LRP), and we find that our approach provides more accurate explanations than
existing methods for ViTs.
