Web: http://arxiv.org/abs/2206.05075

June 17, 2022, 1:12 a.m. | Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller, Pan Kessel

cs.LG updates on arXiv.org arxiv.org

Counterfactuals can explain classification decisions of neural networks in a
human interpretable way. We propose a simple but effective method to generate
such counterfactuals. More specifically, we perform a suitable diffeomorphic
coordinate transformation and then perform gradient ascent in these coordinates
to find counterfactuals which are classified with great confidence as a
specified target class. We propose two methods to leverage generative models to
construct such suitable coordinate systems that are either exactly or
approximately diffeomorphic. We analyze the generation …

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