{"total":65,"rows":[{"id":"2207.13243","title":"Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks","created":"2022-07-27","cited_by_count":129,"area":null},{"id":"2112.03184","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","created":"2021-12-06","cited_by_count":105,"area":null},{"id":"2112.02902","title":"Interpretable Image Classification with Differentiable Prototypes Assignment","created":"2021-12-06","cited_by_count":97,"area":null},{"id":"2111.02398","title":"Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods","created":"2021-11-01","cited_by_count":83,"area":null},{"id":"2205.15612","title":"GlanceNets: Interpretabile, Leak-proof Concept-based Models","created":"2022-05-31","cited_by_count":73,"area":null},{"id":"2210.03735","title":"\"Help Me Help the AI\": Understanding How Explainability Can Support Human-AI Interaction","created":"2022-10-02","cited_by_count":63,"area":null},{"id":"2207.14526","title":"Leveraging Explanations in Interactive Machine Learning: An Overview","created":"2022-07-29","cited_by_count":53,"area":null},{"id":"2205.15769","title":"Concept-level Debugging of Part-Prototype Networks","created":"2022-05-31","cited_by_count":52,"area":null},{"id":"2207.09615","title":"Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability","created":"2022-07-20","cited_by_count":46,"area":null},{"id":"2212.05946","title":"Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks","created":"2022-12-12","cited_by_count":37,"area":null},{"id":"2205.04766","title":"Explainable Deep Learning Methods in Medical Image Classification: A Survey","created":"2022-05-10","cited_by_count":32,"area":null},{"id":"2308.08162","title":"Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2303.07811","title":"ICICLE: Interpretable Class Incremental Continual Learning","created":"2023-03-14","cited_by_count":24,"area":null},{"id":"2108.10612","title":"ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification","created":"2021-08-24","cited_by_count":22,"area":null},{"id":"2309.07742","title":"Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning","created":"2023-09-14","cited_by_count":22,"area":null},{"id":"2403.14349","title":"On the Concept Trustworthiness in Concept Bottleneck Models","created":"2024-03-21","cited_by_count":22,"area":"lr"},{"id":"2309.14531","title":"Pixel-Grounded Prototypical Part Networks","created":"2023-09-25","cited_by_count":18,"area":null},{"id":"2409.13456","title":"Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?","created":"2024-09-20","cited_by_count":18,"area":null},{"id":"2307.14517","title":"The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers","created":"2023-07-26","cited_by_count":15,"area":null},{"id":"2109.09658","title":"FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging","created":"2021-09-20","cited_by_count":13,"area":null},{"id":"2503.01087","title":"Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time","created":"2025-03-03","cited_by_count":13,"area":null},{"id":"2310.06966","title":"On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis","created":"2023-10-10","cited_by_count":11,"area":null},{"id":"2310.00164","title":"PRIME: Prioritizing Interpretability in Failure Mode Extraction","created":"2023-09-29","cited_by_count":9,"area":null},{"id":"2312.13839","title":"Q-SENN: Quantized Self-Explaining Neural Networks","created":"2023-12-21","cited_by_count":9,"area":null},{"id":"2311.16120","title":"Sanity checks for patch visualisation in prototype-based image classification","created":"2023-10-25","cited_by_count":8,"area":null},{"id":"2406.10025","title":"ProtoS-ViT: Visual foundation models for sparse self-explainable classifications","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2303.13166","title":"Take 5: Interpretable Image Classification with a Handful of Features","created":"2023-03-23","cited_by_count":7,"area":null},{"id":"2311.14435","title":"Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces","created":"2023-11-24","cited_by_count":7,"area":null},{"id":"2311.15438","title":"ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]","created":"2023-11-26","cited_by_count":7,"area":null},{"id":"2509.17050","title":"Geodesic Prototype Matching via Diffusion Maps for Interpretable Fine-Grained Recognition","created":"2025-09-21","cited_by_count":6,"area":null},{"id":"2402.17863","title":"Vision Transformers with Natural Language Semantics","created":"2024-02-27","cited_by_count":5,"area":null},{"id":"2410.12284","title":"Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting","created":"2024-10-16","cited_by_count":5,"area":null},{"id":"2109.11160","title":"Toward a Unified Framework for Debugging Concept-based Models","created":"2021-09-23","cited_by_count":4,"area":null},{"id":"2302.08508","title":"Sanity checks and improvements for patch visualisation in prototype-based image classification","created":"2023-01-20","cited_by_count":3,"area":null},{"id":"2311.06647","title":"Robust Text Classification: Analyzing Prototype-Based Networks","created":"2023-11-11","cited_by_count":3,"area":null},{"id":"2407.12200","title":"This Probably Looks Exactly Like That: An Invertible Prototypical Network","created":"2024-07-16","cited_by_count":3,"area":null},{"id":"2407.15871","title":"Semantic Prototypes: Enhancing Transparency Without Black Boxes","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2409.16693","title":"CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models","created":"2024-09-25","cited_by_count":3,"area":"lr"},{"id":"2410.19856","title":"Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2503.08636","title":"Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2310.17010","title":"This Reads Like That: Deep Learning for Interpretable Natural Language Processing","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2403.04125","title":"ComFe: An Interpretable Head for Vision Transformers","created":"2024-03-07","cited_by_count":2,"area":null},{"id":"2412.15499","title":"A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations","created":"2024-12-20","cited_by_count":2,"area":null},{"id":"2502.19577","title":"Tell me why: Visual foundation models as self-explainable classifiers","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2502.20130","title":"QPM: Discrete Optimization for Globally Interpretable Image Classification","created":"2025-02-27","cited_by_count":2,"area":null},{"id":"2303.06635","title":"Schema Inference for Interpretable Image Classification","created":"2023-03-12","cited_by_count":1,"area":null},{"id":"2409.12883","title":"Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition","created":"2024-09-19","cited_by_count":1,"area":null},{"id":"2504.08602","title":"On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2507.06819","title":"Comprehensive Evaluation of Prototype Neural Networks","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2510.25512","title":"FaCT: Faithful Concept Traces for Explaining Neural Network Decisions","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2512.07981","title":"CIP-Net: Continual Interpretable Prototype-based Network","created":"2025-12-08","cited_by_count":1,"area":null},{"id":"2603.08374","title":"This Looks Distinctly Like That: Grounding Interpretable Recognition in Stiefel Geometry against Neural Collapse","created":"2026-03-09","cited_by_count":1,"area":null},{"id":"2201.07934","title":"Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks","created":"2022-01-03","cited_by_count":0,"area":null},{"id":"2404.02830","title":"Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2409.02335","title":"What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits","created":"2024-09-03","cited_by_count":0,"area":null},{"id":"2501.17328","title":"SIC: Similarity-Based Interpretable Image Classification with Neural Networks","created":"2025-01-28","cited_by_count":0,"area":null},{"id":"2504.19357","title":"MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis","created":"2025-04-27","cited_by_count":0,"area":null},{"id":"2507.13727","title":"Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2507.14706","title":"Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling","created":"2025-07-19","cited_by_count":0,"area":null},{"id":"2510.00664","title":"Batch-CAM: Introduction to better reasoning in convolutional deep learning models","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2510.00683","title":"ProtoMask: Segmentation-Guided Prototype Learning","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2512.17891","title":"Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Training","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2604.07166","title":"DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.06095","title":"Metonymy in vision models undermines attention-based interpretability","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.13277","title":"ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization","created":"2026-06-11","cited_by_count":0,"area":null}],"next_cursor":null}