Gradient strikes back: How filtering out high frequencies improves explanations

arXiv:2307.09591v2 Announce Type: replace-cross
Abstract: Attribution methods correspond to a class of explainability methods (XAI) that aim to assess how individual inputs contribute to a model's decision-making process. We have identified a significant limitation in one type of attribution methods, known as "white-box" methods. Although highly efficient, these methods rely on a gradient signal that is often contaminated by high-frequency noise. To overcome this limitation, we introduce a new approach called "FORGrad". This simple method effectively filters out noise artifacts …

abstract aim arxiv attribution box class cs.ai cs.cv cs.lg decision explainability filtering gradient inputs making process strikes type xai