Spectral Integrated Gradients for Coarse-to-Fine Feature Attribution
Integrated Gradients (IG) is a widely adopted feature attribution method that satisfies desirable axiomatic properties. However, the choice of integration path significantly affects the quality of attributions, and the standard straight-line path introduces all input features simultaneously, often accumulating noisy gradients along the way. To address this limitation, we propose Spectral Integrated Gradients, which constructs integration paths based on singular value decomposition (SVD) of the baseline-to-input difference. By progressively activating singular components from largest to smallest, SIG introduces global structure before fine-grained details, naturally following a coarse-to-f...
| Authors | Soyeon Kim, Seongwoo Lim, Kyowoon Lee et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 5 August 2026 |
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