Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations
The increasing complexity of state-of-the-art machine learning models has made their behavior progressively harder to interpret, spurring rapid advancements in the field of eXplainable Artificial Intelligence (XAI). Among many methods proposed, perturbation-based approaches play a major role. By systematically altering (perturbing) input features, these approaches measure the impact on the model's predictions. For image data, traditional perturbation techniques, often involve replacing pixel values e.g., with a pre-defined color. However, such approaches, but also more refined deterministic techniques, generate unrealistic out-of-distribution samples and often leave visible artifacts, whi...
| Authors | Josef Lindl, Mariana Chaves, Damien Garreau |
|---|---|
| Primary category | cs.LG |
| Published | 16 July 2026 |
| Last updated at source | 14 October 2026 |
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