{"total":19,"rows":[{"id":"2302.07265","title":"The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus","created":"2023-02-14","cited_by_count":37,"area":null},{"id":"2401.06654","title":"Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks","created":"2024-01-12","cited_by_count":28,"area":null},{"id":"2303.00652","title":"Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science","created":"2023-03-01","cited_by_count":27,"area":null},{"id":"2205.13042","title":"How explainable are adversarially-robust CNNs?","created":"2022-05-25","cited_by_count":9,"area":null},{"id":"2407.11910","title":"Benchmarking the Attribution Quality of Vision Models","created":"2024-07-16","cited_by_count":8,"area":null},{"id":"2306.12816","title":"XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance","created":"2023-06-22","cited_by_count":7,"area":null},{"id":"2504.09621","title":"Tokenize Image Patches: Global Context Fusion for Effective Haze Removal in Large Images","created":"2025-04-13","cited_by_count":7,"area":null},{"id":"2404.11330","title":"Toward Understanding the Disagreement Problem in Neural Network Feature Attribution","created":"2024-04-17","cited_by_count":6,"area":null},{"id":"2411.01710","title":"SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation","created":"2024-11-03","cited_by_count":6,"area":null},{"id":"2509.26543","title":"The Unheard Alternative: Contrastive Explanations for Speech-to-Text Models","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2307.01777","title":"Shapley Sets: Feature Attribution via Recursive Function Decomposition","created":"2023-07-04","cited_by_count":2,"area":null},{"id":"2307.07764","title":"Explaining and visualizing black-box models through counterfactual paths","created":"2023-07-15","cited_by_count":2,"area":null},{"id":"2411.00818","title":"On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications","created":"2024-10-28","cited_by_count":2,"area":null},{"id":"2508.07636","title":"Attribution Explanations for Deep Neural Networks: A Theoretical Perspective","created":"2025-08-11","cited_by_count":2,"area":null},{"id":"2602.09278","title":"The effect of whitening on explanation performance","created":"2026-02-09","cited_by_count":1,"area":null},{"id":"2211.12702","title":"Evaluating Feature Attribution Methods for Electrocardiogram","created":"2022-11-23","cited_by_count":0,"area":null},{"id":"2310.12553","title":"Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2605.05480","title":"GRALIS: Fusing Coalition and Gradient Attribution with Closed-Form Conservation Error and Finite-Sample Guarantees","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2606.10877","title":"XtrAIn: Training-Guided Occlusion for Feature Attribution","created":"2026-06-09","cited_by_count":0,"area":null}],"next_cursor":null}