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To better understand the output of deep neural networks (DNN), attributionbased methods have been an important approach for model interpretability, whichassign a score for each input dimension to indicate its importance towards themodel outcome. Notably, the attribution methods use the axioms of sensitivityand implementation invariance to ensure the validity and reliability ofattribution results. Yet, the existing attribution methods present challengesfor effective interpretation and efficient computation. In this work, weintroduce MFABA, an attribution algorithm that adheres to axioms, as a novelmethod for interpreting DNN. Additionally, we provide the theoretical proof andin-depth analysis for MFABA algorithm, and conduct a large scale experiment.The results demonstrate its superiority by achieving over 101.5142 times fasterspeed than the state-of-the-art attribution algorithms. The effectiveness ofMFABA is thoroughly evaluated through the statistical analysis in comparison toother methods, and the full implementation package is open-source at:https://github.com/LMBTough/MFABA

MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks