  When an image classifier outputs a wrong class label, it can be helpful to
see what changes in the image would lead to a correct classification. This is
the aim of algorithms generating counterfactual explanations. However, there is
no easily scalable method to generate such counterfactuals. We develop a new
algorithm providing counterfactual explanations for large image classifiers
trained with spectral normalisation at low computational cost. We empirically
compare this algorithm against baselines from the literature; our novel
algorithm consistently finds counterfactuals that are much closer to the
original inputs. At the same time, the realism of these counterfactuals is
comparable to the baselines. The code for all experiments is available at
https://github.com/benedikthoeltgen/DeDUCE.
