DeDUCE: Generating Counterfactual Explanations Efficiently

Understand

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.