mlsanityData to 14 Oct 2026

DeDUCE: Generating Counterfactual Explanations Efficiently

arXiv 2111.15639 ↗First posted 29 Nov 2021cs.CV cs.LG stat.MLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 29 Nov 2021.

Builds on

20 of 31 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  5. 2017Counterfactual Fairnesscited by 804
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  9. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  10. 2019Counterfactual Visual Explanationscited by 313
  11. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 262
  12. 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
8 more
  1. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  2. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 131
  3. 2020A survey of algorithmic recourse: definitions, formulations, solutions, and prospectscited by 118
  4. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  5. 2020On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learningcited by 60
  6. 2020The Intriguing Relation Between Counterfactual Explanations and Adversarial Examplescited by 44
  7. 2021Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertaintiescited by 41
  8. 2018Representing smooth functions as compositions of near-identity functions with implications for deep network optimizationcited by 15

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Cited by year

Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  2. 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomenacited by 21
  3. 2023Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAIcited by 20
  4. 2022Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differcited by 12
  5. 2022Clarity: an improved gradient method for producing quality visual counterfactual explanationscited by 0
  6. 2023Two-step counterfactual generation for OOD examplescited by 0