DeepAI AI Chat
Log In Sign Up

DeDUCE: Generating Counterfactual Explanations Efficiently

by   Benedikt Höltgen, et al.

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


page 8

page 12

page 13


Generating Counterfactual Explanations with Natural Language

Natural language explanations of deep neural network decisions provide a...

NICE: An Algorithm for Nearest Instance Counterfactual Explanations

In this paper we suggest NICE: a new algorithm to generate counterfactua...

Sparse Visual Counterfactual Explanations in Image Space

Visual counterfactual explanations (VCEs) in image space are an importan...

ECINN: Efficient Counterfactuals from Invertible Neural Networks

Counterfactual examples identify how inputs can be altered to change the...

Diffusion Visual Counterfactual Explanations

Visual Counterfactual Explanations (VCEs) are an important tool to under...

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

Counterfactual explanations are viewed as an effective way to explain ma...

Prototype-based Counterfactual Explanation for Causal Classification

Counterfactual explanation is one branch of interpretable machine learni...