mlsanityData to 7 Oct 2026

SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems

arXiv 2307.15786 ↗First posted 28 Jul 2023cs.LG cs.AI cs.LOcited by 5

Not on the map. Why?

Abstract (arXiv)

A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep generative CF models often work with user-selected features rather than focusing on the discriminative features of the black-box model. Consequently, such CF examples may not necessarily lie near the decision boundary, thereby contradicting the definition of CFs. To address this issue, we propose in this paper a novel approach that leverages saliency maps to generate more informative CF explanations. Source codes are available at: https://github.com/Amir-Samadi//Saliency_Aware_CF.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 48 references that are in the map. First posted on arXiv 28 Jul 2023.

Builds on

40 of 48 references.

References · 40

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2017Improved Training of Wasserstein GANscited by 2,223
  7. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  8. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  9. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  10. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  11. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  12. 2019Object Detection in 20 Years: A Surveycited by 528
28 more
  1. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 416
  2. 2017AttGAN: Facial Attribute Editing by Only Changing What You Wantcited by 241
  3. 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
  4. 2020Multi-Objective Counterfactual Explanationscited by 157
  5. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  6. 2017Interpretable Learning for Self-Driving Cars by Visualizing Causal Attentioncited by 106
  7. 2022Diffusion Visual Counterfactual Explanationscited by 96
  8. 2020Explainable Object-induced Action Decision for Autonomous Vehiclescited by 88
  9. 2019On the Connection Between Adversarial Robustness and Saliency Map Interpretabilitycited by 80
  10. 2022Diffusion Models for Counterfactual Explanationscited by 79
  11. 2019AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networkscited by 77
  12. 2019Grounding Human-to-Vehicle Advice for Self-driving Vehiclescited by 71
  13. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  14. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  15. 2017Applications of Deep Learning and Reinforcement Learning to Biological Datacited by 45
  16. 2020Counterfactual Fairness with Disentangled Causal Effect Variational Autoencodercited by 45
  17. 2021STEEX: Steering Counterfactual Explanations with Semanticscited by 40
  18. 2019Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translationcited by 39
  19. 2019Generative Counterfactual Introspection for Explainable Deep Learningcited by 36
  20. 2019CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence modelscited by 34
  21. 2022Cycle-Consistent Counterfactuals by Latent Transformationscited by 33
  22. 2022OCTET: Object-aware Counterfactual Explanationscited by 28
  23. 2019Counterfactual Explanation Algorithms for Behavioral and Textual Datacited by 26
  24. 2019Generating Counterfactual and Contrastive Explanations using SHAPcited by 23
  25. 2023GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanationscited by 17
  26. 2020On Saliency Maps and Adversarial Robustnesscited by 11
  27. 2021Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbationcited by 11
  28. 2023Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 3.

5 papers.

  1. 2024SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policiescited by 8
  2. 2024Good Data Is All Imitation Learning Needscited by 2
  3. 2026A Task-Agnostic Algebraic Integrity Metric for Event-Camera Streams Toward SOTIF-Compliant Perception using Pearson Correlation Coefficientcited by 0
  4. 2026Engineering Trustworthy Agentic AI for Critical Systemscited by 0
  5. 2026Concept-based Visual Counterfactual Explanations with Diffusion Modelscited by 0