Explaining an image classifier's decisions using generative models

9 Oct 2019Chirag AgarwalAnh Nguyen

Perturbation-based explanation methods often measure the contribution of an input feature to an image classifier's outputs by heuristically removing it via e.g. blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Instead, we propose to integrate a generative inpainter into three representative attribution methods to remove an input feature... (read more)

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