{"total":86,"rows":[{"id":"2310.19775","title":"Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions","created":"2023-10-30","cited_by_count":125,"area":null},{"id":"2312.12936","title":"Concept-based Explainable Artificial Intelligence: A Survey","created":"2023-12-20","cited_by_count":98,"area":null},{"id":"2112.02902","title":"Interpretable Image Classification with Differentiable Prototypes Assignment","created":"2021-12-06","cited_by_count":97,"area":null},{"id":"2205.15612","title":"GlanceNets: Interpretabile, Leak-proof Concept-based Models","created":"2022-05-31","cited_by_count":73,"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":"2301.04011","title":"Learning Support and Trivial Prototypes for Interpretable Image Classification","created":"2023-01-08","cited_by_count":38,"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":"2310.18589","title":"This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations","created":"2023-10-28","cited_by_count":36,"area":null},{"id":"2210.08151","title":"ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model","created":"2022-10-15","cited_by_count":34,"area":null},{"id":"2301.12276","title":"ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts","created":"2023-01-28","cited_by_count":33,"area":null},{"id":"2503.23283","title":"Language Guided Concept Bottleneck Models for Interpretable Continual Learning","created":"2025-03-30","cited_by_count":28,"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":"2410.20722","title":"Interpretable Image Classification with Adaptive Prototype-based Vision Transformers","created":"2024-10-28","cited_by_count":25,"area":null},{"id":"2303.07811","title":"ICICLE: Interpretable Class Incremental Continual Learning","created":"2023-03-14","cited_by_count":24,"area":null},{"id":"2204.11830","title":"Proto2Proto: Can you recognize the car, the way I do?","created":"2022-04-25","cited_by_count":23,"area":null},{"id":"2310.19906","title":"Interpretable Prototype-based Graph Information Bottleneck","created":"2023-10-30","cited_by_count":23,"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":"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":"2410.02331","title":"Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks","created":"2024-10-03","cited_by_count":18,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","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":"2405.14331","title":"LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision","created":"2024-05-23","cited_by_count":15,"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":"2304.06258","title":"MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging","created":"2023-04-13","cited_by_count":11,"area":null},{"id":"2312.00092","title":"Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition","created":"2023-11-30","cited_by_count":11,"area":null},{"id":"2211.05207","title":"Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning","created":"2022-11-09","cited_by_count":10,"area":null},{"id":"2309.13550","title":"I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses","created":"2023-09-24","cited_by_count":10,"area":null},{"id":"2312.13839","title":"Q-SENN: Quantized Self-Explaining Neural Networks","created":"2023-12-21","cited_by_count":9,"area":null},{"id":"2407.04538","title":"PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers","created":"2024-07-05","cited_by_count":9,"area":null},{"id":"2210.03745","title":"ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction","created":"2022-10-07","cited_by_count":8,"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":"2409.10329","title":"InfoDisent: Explainability of Image Classification Models by Information Disentanglement","created":"2024-09-16","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":"2311.15438","title":"ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]","created":"2023-11-26","cited_by_count":7,"area":null},{"id":"2406.03421","title":"Post-hoc Part-prototype Networks","created":"2024-06-05","cited_by_count":7,"area":null},{"id":"2312.07822","title":"Prototypical Self-Explainable Models Without Re-training","created":"2023-12-13","cited_by_count":6,"area":null},{"id":"2507.23784","title":"SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions","created":"2025-07-31","cited_by_count":6,"area":null},{"id":"2511.16588","title":"Formal Abductive Latent Explanations for Prototype-Based Networks","created":"2025-11-20","cited_by_count":5,"area":null},{"id":"2409.09497","title":"Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation","created":"2024-09-14","cited_by_count":4,"area":null},{"id":"2507.19321","title":"SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence","created":"2025-07-25","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":"2306.03022","title":"Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder","created":"2023-06-05","cited_by_count":3,"area":null},{"id":"2308.13651","title":"PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans","created":"2023-08-25","cited_by_count":3,"area":null},{"id":"2403.20260","title":"Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges","created":"2024-03-29","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":"2412.07802","title":"Language Model as Visual Explainer","created":"2024-12-08","cited_by_count":3,"area":null},{"id":"2502.09340","title":"This looks like what? Challenges and Future Research Directions for Part-Prototype Models","created":"2025-02-13","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":"2506.05533","title":"Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2106.11054","title":"Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations","created":"2021-06-21","cited_by_count":2,"area":null},{"id":"2211.04509","title":"Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2302.03318","title":"PAMI: partition input and aggregate outputs for model interpretation","created":"2023-02-07","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":"2404.15037","title":"DP-Net: Learning Discriminative Parts for image recognition","created":"2024-04-23","cited_by_count":2,"area":null},{"id":"2408.11401","title":"Revisiting FunnyBirds evaluation framework for prototypical parts networks","created":"2024-08-21","cited_by_count":2,"area":null},{"id":"2412.02399","title":"OMENN: One Matrix to Explain Neural Networks","created":"2024-12-03","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":"2503.06084","title":"Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2303.02302","title":"Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation","created":"2023-03-04","cited_by_count":1,"area":null},{"id":"2402.19142","title":"ProtoP-OD: Explainable Object Detection with Prototypical Parts","created":"2024-02-29","cited_by_count":1,"area":null},{"id":"2406.08457","title":"ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery","created":"2024-06-12","cited_by_count":1,"area":null},{"id":"2408.04606","title":"Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes","created":"2024-08-08","cited_by_count":1,"area":null},{"id":"2410.18705","title":"Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks","created":"2024-10-24","cited_by_count":1,"area":null},{"id":"2504.12197","title":"Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2505.17666","title":"Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2506.19808","title":"ProtoSolo: Interpretable Image Classification via Single-Prototype Activation","created":"2025-06-24","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":"2205.10724","title":"Learnable Visual Words for Interpretable Image Recognition","created":"2022-05-22","cited_by_count":0,"area":null},{"id":"2305.10121","title":"FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks","created":"2023-05-17","cited_by_count":0,"area":null},{"id":"2312.01871","title":"FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2403.15765","title":"Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents","created":"2024-03-23","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":"2410.00524","title":"Deep Model Interpretation with Limited Data : A Coreset-based Approach","created":"2024-10-01","cited_by_count":0,"area":null},{"id":"2503.12978","title":"Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach","created":"2025-03-17","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":"2511.19752","title":"What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2512.10720","title":"Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.06592","title":"ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.10239","title":"XSPLAIN: XAI-enabling Splat-based Prototype Learning for Attribute-aware INterpretability","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2605.08858","title":"ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2608.30003","title":"OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.27194","title":"Diverse by Design: Architectural Constraints for Prototype-Based Interpretability","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}