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:
this https URL

深度神经网络的解释性和清晰度对于产生人类信任感都至关重要。本研究提出了一种集中分配高度解释性到不可或缺特征的新原则，并通过构建 SAMP 模型解释器来有效地搜索最优路径。视觉展示表明，SAMP 能够通过定位显著的图像像素来精确揭示深度神经网络。此外，定量实验证明我们的方法明显优于其他方法。