mlsanityData to 7 Oct 2026

IROF: a low resource evaluation metric for explanation methods

arXiv 2003.08747 ↗First posted 9 Mar 2020cs.CVcited by 45

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Abstract (arXiv)

The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing literature on explaining neural networks, no consensus has been reached on how to evaluate those explanation methods. We propose IROF, a new approach to evaluating explanation methods that circumvents the need for manual evaluation. Compared to other recent work, our approach requires several orders of magnitude less computational resources and no human input, making it accessible to lower resource groups and robust to human bias.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 10; 2024: 15; 2025: 8; 2026: 3.

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  1. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  2. 2023The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantuscited by 37
  3. 2024PEDANTS: Cheap but Effective and Interpretable Answer Equivalencecited by 31
  4. 2024Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarkscited by 28
  5. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
  6. 2022Xplique: A Deep Learning Explainability Toolboxcited by 24
  7. 2022Evaluating Feature Attribution Methods in the Image Domaincited by 19
  8. 2023Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasetscited by 15
  9. 2024Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metricscited by 15
  10. 2024A comprehensive study on fidelity metrics for XAIcited by 14
  11. 2021RELAX: Representation Learning Explainabilitycited by 12
  12. 2023Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural Networkscited by 9
33 more
  1. 2024Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Testcited by 9
  2. 2023Causal Analysis for Robust Interpretability of Neural Networkscited by 8
  3. 2024A Fresh Look at Sanity Checks for Saliency Mapscited by 8
  4. 2024Benchmarking XAI Explanations with Human-Aligned Evaluationscited by 8
  5. 2023An adversarial attack approach for eXplainable AI evaluation on deepfake detection modelscited by 7
  6. 2024Benchmarking the Attribution Quality of Vision Modelscited by 7
  7. 2024Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanationcited by 6
  8. 2022On the Robustness of Explanations of Deep Neural Network Models: A Surveycited by 5
  9. 2024Expected Grad-CAM: Towards gradient faithfulnesscited by 4
  10. 2024From Flexibility to Manipulation: The Slippery Slope of XAI Evaluationcited by 4
  11. 2021Spatio-Temporal Perturbations for Video Attributioncited by 3
  12. 2023Robustness of Visual Explanations to Common Data Augmentationcited by 3
  13. 2025On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classificationcited by 3
  14. 2021A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machinescited by 2
  15. 2022Fidelity of Ensemble Aggregation for Saliency Map Explanations using Bayesian Optimization Techniquescited by 1
  16. 2024Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluationcited by 1
  17. 2024Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquirycited by 1
  18. 2025Fast Fourier Correlation is a Highly Efficient and Accurate Feature Attribution Algorithm from the Perspective of Control Theory and Game Theorycited by 1
  19. 2025Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-beingcited by 1
  20. 2025Back to the Baseline: Examining Baseline Effects on Explainability Metricscited by 1
  21. 2022Relational Local Explanationscited by 0
  22. 2023Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistencycited by 0
  23. 2023Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizerscited by 0
  24. 2023Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspectivecited by 0
  25. 2024Challenges in explaining deep learning models for data with biological variationcited by 0
  26. 2024Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insightscited by 0
  27. 2025Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distributioncited by 0
  28. 2025Metric-Guided Synthesis of Class Activation Mappingcited by 0
  29. 2025DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanationscited by 0
  30. 2025Value bounds and Convergence Analysis for Averages of LRP attributionscited by 0
  31. 2026AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detectioncited by 0
  32. 2026Structural Compactness as a Complementary Criterion for Explanation Qualitycited by 0
  33. 2026Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbationscited by 0