23 citations · 77 across the 11 of their papers we have counts for
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cs.CV2021★ 2 cited
DeDUCE: Generating Counterfactual Explanations Efficiently
Benedikt Höltgen, Lisa Schut, Jan M. Brauner +1
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 gener…
cs.LG2021★ 8 cited
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, Rory McGrath +4
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they…