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Generative Counterfactual Introspection for Explainable Deep Learning

arXiv 1907.03077 ↗First posted 6 Jul 2019cs.LG cs.AI cs.CVcited by 36

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Abstract (arXiv)

In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation. Such modification provides the fundamental interventional operation that allows us to obtain answers to counterfactual inquiries, i.e., what meaningful change can be made to the input image in order to alter the prediction. We demonstrate how to reveal interesting properties of the given classifiers by utilizing the proposed introspection approach on both the MNIST and the CelebA dataset.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2019.

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Citing papers per year: 2021: 17; 2022: 11; 2023: 2; 2024: 2; 2025: 3; 2026: 1.

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  1. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  2. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  3. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  4. 2022Diffusion Models for Counterfactual Explanationscited by 79
  5. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  6. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  7. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  8. 2022Cycle-Consistent Counterfactuals by Latent Transformationscited by 33
  9. 2022Making Heads or Tails: Towards Semantically Consistent Visual Counterfactualscited by 30
  10. 2022Diffeomorphic Counterfactuals with Generative Modelscited by 29
  11. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  12. 2021Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learningcited by 15
24 more
  1. 2022Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Datasetcited by 13
  2. 2022MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanationcited by 12
  3. 2021Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbationcited by 11
  4. 2022Interpretability Guarantees with Merlin-Arthur Classifierscited by 8
  5. 2021Causal Learning for Socially Responsible AIcited by 7
  6. 2022Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rayscited by 7
  7. 2021Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Networkcited by 5
  8. 2023SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systemscited by 5
  9. 2022Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spacescited by 4
  10. 2023Two-Stage Holistic and Contrastive Explanation of Image Classificationcited by 4
  11. 2025LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matchingcited by 4
  12. 2021Reliable Graph Neural Network Explanations Through Adversarial Trainingcited by 3
  13. 2024Counterfactual Explanations for Deep Learning-Based Traffic Forecastingcited by 3
  14. 2021Discriminative Attribution from Counterfactualscited by 2
  15. 2021Explaining the Black-box Smoothly- A Counterfactual Approachcited by 1
  16. 2021Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Modelscited by 1
  17. 2021Explainability Requires Interactivitycited by 1
  18. 2021DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Losscited by 0
  19. 2021VAE-CE: Visual Contrastive Explanation using Disentangled VAEscited by 0
  20. 2022Clarity: an improved gradient method for producing quality visual counterfactual explanationscited by 0
  21. 2024CF-OPT: Counterfactual Explanations for Structured Predictioncited by 0
  22. 2025Explaining Low Perception Model Competency with High-Competency Counterfactualscited by 0
  23. 2025Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attackscited by 0
  24. 2026U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanationscited by 0