홈 › Papers

SLICE: Stabilized LIME for Consistent Explanations for Image Classification

Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations are then extracted for a black-box model and a given IE using the surrogate model. However the explanations of LIME suffer from inconsistency across different runs for the same model and the same IE. We identify two main types of inconsistencies: variance in the sign and importance ranks of the segments (superpixels). These factors hinder LIME from obtaining consistent explanations. We analyze these inconsistencies and propose a new method Stabilized LIME for Consistent Explanations (SLICE). The proposed method handles the stabilization problem in two aspects: using a novel feature selection technique to eliminate spurious superpixels and an adaptive perturbation technique to generate perturbed images in the neighborhood of IE. Our results demonstrate that the explanations from SLICE exhibit significantly better consistency and fidelity than LIME (and its variant BayLime).

Code (1)

Tasks

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반