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Explainable artificial intelligence (XAI) holds immense significance inenhancing the deep neural network's transparency and credibility, particularlyin some risky and high-cost scenarios, like synthetic aperture radar (SAR).Shapley is a game-based explanation technique with robust mathematicalfoundations. However, Shapley assumes that model's features are independent,rendering Shapley explanation invalid for high dimensional models. This studyintroduces a manifold-based Shapley method by projecting high-dimensionalfeatures into low-dimensional manifold features and subsequently obtainingFusion-Shap, which aims at (1) addressing the issue of erroneous explanationsencountered by traditional Shap; (2) resolving the challenge ofinterpretability that traditional Shap faces in complex scenarios.

Manifold-based Shapley for SAR Recognization Network Explanation