mlsanityData to 7 Oct 2026

RISE: Randomized Input Sampling for Explanation of Black-box Models

arXiv 1806.07421 ↗First posted 19 Jun 2018cs.CVcited by 825

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Cited by 30 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on black-box models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches. Project page: http://cs-people.bu.edu/vpetsiuk/rise/

Key facts. Cited by 825 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 84; 2022: 128; 2023: 162; 2024: 167; 2025: 141; 2026: 143.

825 papers.

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  1. 2023SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generationcited by 376
  2. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  3. 2022CRAFT: Concept Recursive Activation FacTorization for Explainabilitycited by 177
  4. 2022The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspectivecited by 156
  5. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  6. 2022OpenXAI: Towards a Transparent Evaluation of Model Explanationscited by 125
  7. 2021FastSHAP: Real-Time Shapley Value Estimationcited by 123
  8. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  9. 2023A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimationcited by 108
  10. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  11. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  12. 2024Interpreting and Editing Vision-Language Representations to Mitigate Hallucinationscited by 103
38 more
  1. 2021Guided Integrated Gradients: An Adaptive Path Method for Removing Noisecited by 102
  2. 2022A Consistent and Efficient Evaluation Strategy for Attribution Methodscited by 100
  3. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  4. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  5. 2022B-cos Networks: Alignment is All We Need for Interpretabilitycited by 88
  6. 2021The effectiveness of feature attribution methods and its correlation with automatic evaluation scorescited by 87
  7. 2022Diffusion Models for Counterfactual Explanationscited by 79
  8. 2022Impossibility Theorems for Feature Attributioncited by 78
  9. 2022Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanationscited by 77
  10. 2023On Measuring Faithfulness or Self-consistency of Natural Language Explanationscited by 71
  11. 2021Improving Deep Learning Interpretability by Saliency Guided Trainingcited by 68
  12. 2025Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Modelscited by 67
  13. 2021Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysiscited by 65
  14. 2022A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributionscited by 64
  15. 2023On the Foundations of Shortcut Learningcited by 64
  16. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  17. 2021Convolutional Dynamic Alignment Networks for Interpretable Classificationscited by 54
  18. 2021Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAycited by 53
  19. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  20. 2024Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Surveycited by 52
  21. 2023Learning Bottleneck Concepts in Image Classificationcited by 49
  22. 2023FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methodscited by 49
  23. 2021Towards Better Explanations of Class Activation Mappingcited by 47
  24. 2021ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenescited by 47
  25. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  26. 2022Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capabilitycited by 46
  27. 2023Rosetta Neurons: Mining the Common Units in a Model Zoocited by 46
  28. 2022Learning to Estimate Shapley Values with Vision Transformerscited by 44
  29. 2022MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Taskscited by 42
  30. 2023Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistencycited by 42
  31. 2022Metrics for saliency map evaluation of deep learning explanation methodscited by 40
  32. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  33. 2022Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysiscited by 38
  34. 2022Large Loss Matters in Weakly Supervised Multi-Label Classificationcited by 38
  35. 2023Learning Support and Trivial Prototypes for Interpretable Image Classificationcited by 38
  36. 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanationcited by 37
  37. 2021Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networkscited by 36
  38. 2022WeightedSHAP: analyzing and improving Shapley based feature attributionscited by 36

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