Explaining an image classifier with a generative model conditioned by uncertainty

Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed

Abstract: We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a corrupted version of MNIST dataset illustrate the idea.