{"total":23,"rows":[{"id":"2112.03184","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","created":"2021-12-06","cited_by_count":105,"area":null},{"id":"2202.00734","title":"Framework for Evaluating Faithfulness of Local Explanations","created":"2022-02-01","cited_by_count":55,"area":null},{"id":"2301.07002","title":"Opti-CAM: Optimizing saliency maps for interpretability","created":"2023-01-17","cited_by_count":25,"area":null},{"id":"2506.11136","title":"JAFAR: Jack up Any Feature at Any Resolution","created":"2025-06-10","cited_by_count":21,"area":null},{"id":"2204.05376","title":"medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space","created":"2022-04-11","cited_by_count":10,"area":null},{"id":"2407.13281","title":"Auditing Local Explanations is Hard","created":"2024-07-18","cited_by_count":8,"area":null},{"id":"2501.06261","title":"CAMs as Shapley Value-based Explainers","created":"2025-01-09","cited_by_count":5,"area":null},{"id":"2310.02588","title":"ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformer","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2404.02388","title":"CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation","created":"2024-04-03","cited_by_count":4,"area":null},{"id":"2411.14946","title":"Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach","created":"2024-11-22","cited_by_count":4,"area":null},{"id":"2209.14074","title":"Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks","created":"2022-09-28","cited_by_count":3,"area":null},{"id":"2210.04533","title":"Local Interpretable Model Agnostic Shap Explanations for machine learning models","created":"2022-10-10","cited_by_count":3,"area":null},{"id":"2309.14304","title":"Overview of Class Activation Maps for Visualization Explainability","created":"2023-09-25","cited_by_count":2,"area":null},{"id":"2311.12860","title":"On the stability, correctness and plausibility of visual explanation methods based on feature importance","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2402.11670","title":"Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry","created":"2024-02-18","cited_by_count":2,"area":null},{"id":"2308.03486","title":"Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2312.14201","title":"Towards Better Visualizing the Decision Basis of Networks via Unfold and Conquer Attribution Guidance","created":"2023-12-21","cited_by_count":1,"area":null},{"id":"2601.12804","title":"SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability","created":"2026-01-19","cited_by_count":1,"area":null},{"id":"2604.23335","title":"H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading","created":"2026-04-25","cited_by_count":1,"area":null},{"id":"2503.20230","title":"TraNCE: Transformative Non-linear Concept Explainer for CNNs","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2507.23709","title":"Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2602.19274","title":"DD-CAM: Minimal Sufficient Explanations for Vision Models Using Delta Debugging","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2605.14641","title":"How to Evaluate and Refine your CAM","created":"2026-05-14","cited_by_count":0,"area":null}],"next_cursor":null}