Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution
Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic properties. However, IG can yield unreliable explanations when the integration path between a baseline and the input passes through regions with noisy gradients. While Guided Integrated Gradients reduces this sensitivity by adaptively updating low-gradient-magnitude features, input-space guidance still produces intermediate inputs that deviate from the data manifold. To address this limitation, we propose \emph{Manifold-Aligned Guided Integrated Gradients} (MA-GIG), which constructs attribution paths in the latent space of a pre-trained variatio...
| Authors | Soyeon Kim, Seongwoo Lim, Kyowoon Lee et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 17 May 2026 |
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