Path Choice Matters for Clear Attribution in Path Methods
Borui Zhang Wenzhao Zheng Jie Zhou Jiwen Lu
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.