mlsanityData to 13 Oct 2026

Methods for Interpreting and Understanding Deep Neural Networks

arXiv 1706.07979 ↗First posted 24 Jun 2017cs.LG stat.MLcited by 344

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Abstract (arXiv)

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.

Key facts. Cited by 344 papers in the map (data through 13 Oct 2026). First posted on arXiv 24 Jun 2017.

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Citing papers per year: 2020: 1; 2021: 93; 2022: 74; 2023: 55; 2024: 66; 2025: 30; 2026: 25.

344 papers.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 300
  2. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 192
  3. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  4. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  5. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  6. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  7. 2022XAI for Transformers: Better Explanations through Conservative Propagationcited by 100
  8. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  9. 2021Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Modelscited by 78
  10. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 76
  11. 2022On scientific understanding with artificial intelligencecited by 68
  12. 2024Attention Heads of Large Language Models: A Surveycited by 67
38 more
  1. 2021On Explaining Random Forests with SATcited by 57
  2. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  3. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 53
  4. 2023Multi-dimensional concept discovery (MCD): A unifying framework with completeness guaranteescited by 50
  5. 2022Logic-Based Explainability in Machine Learningcited by 48
  6. 2022Criteria for Classifying Forecasting Methodscited by 48
  7. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  8. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  9. 2022On Tackling Explanation Redundancy in Decision Treescited by 44
  10. 2021This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagationcited by 43
  11. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 42
  12. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  13. 2022A Survey on Explainable Anomaly Detectioncited by 39
  14. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  15. 2021On Efficiently Explaining Graph-Based Classifierscited by 37
  16. 2022On the Explainability of Natural Language Processing Deep Modelscited by 37
  17. 2022Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networkscited by 37
  18. 2023The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantuscited by 37
  19. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  20. 2023Auxiliary Losses for Learning Generalizable Concept-based Modelscited by 35
  21. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  22. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 32
  23. 2021Toward Explainable AI for Regression Modelscited by 31
  24. 2024MambaLRP: Explaining Selective State Space Sequence Modelscited by 31
  25. 2021NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weightscited by 28
  26. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  27. 2024Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarkscited by 28
  28. 2024Bilinear MLPs enable weight-based mechanistic interpretabilitycited by 28
  29. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 26
  30. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 26
  31. 2021Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysiscited by 25
  32. 2021Learning Visual Affordance Grounding from Demonstration Videoscited by 25
  33. 2022Architectural Backdoors in Neural Networkscited by 25
  34. 2022Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspacescited by 25
  35. 2023Opti-CAM: Optimizing saliency maps for interpretabilitycited by 25
  36. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  37. 2024The Clever Hans Effect in Unsupervised Learningcited by 24
  38. 2022Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMFcited by 23

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