Path Choice Matters for Clear Attribution in Path Methods

• URL: http://arxiv.org/abs/2401.10442v1
• Date: Fri, 19 Jan 2024 01:11:44 GMT
• Title: Path Choice Matters for Clear Attribution in Path Methods
• Authors: Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu
• Abstract summary: We introduce textbfConcentration Principle, which allocates high attributions to indispensable features. We then present textbfSAMP, a model-agnostic interpreter, which efficiently searches the near-optimal path. We also propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality.
• Score: 84.29092710376217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy three axioms. However, the meaning of attributions remains ambiguous due to distinct path choices. To address the ambiguity, we introduce \textbf{Concentration Principle}, which centrally allocates high attributions to indispensable features, thereby endowing aesthetic and sparsity. We then present \textbf{SAMP}, a model-agnostic interpreter, which efficiently searches the near-optimal path from a pre-defined set of manipulation paths. Moreover, we propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality. Visualizations show that SAMP can precisely reveal DNNs by pinpointing salient image pixels. We also perform quantitative experiments and observe that our method significantly outperforms the counterparts. Code: https://github.com/zbr17/SAMP.

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