mlsanityData to 7 Oct 2026

ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery

arXiv 2011.14340 ↗First posted 29 Nov 2020cs.CV cs.AI cs.LGcited by 89

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Abstract (arXiv)

In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions. The main novelty of the ProtoPShare is its ability to efficiently share prototypical parts between the classes thanks to our data-dependent merge-pruning. Moreover, the prototypes are more consistent and the model is more robust to image perturbations than the state of the art method ProtoPNet. We verify our findings on two datasets, the CUB-200-2011 and the Stanford Cars.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 10; 2023: 27; 2024: 25; 2025: 20; 2026: 5.

89 papers.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  3. 2023Concept-based Explainable Artificial Intelligence: A Surveycited by 97
  4. 2022GlanceNets: Interpretabile, Leak-proof Concept-based Modelscited by 73
  5. 2022Concept-level Debugging of Part-Prototype Networkscited by 52
  6. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  7. 2023Learning Support and Trivial Prototypes for Interpretable Image Classificationcited by 38
  8. 2022Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networkscited by 37
  9. 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizationscited by 36
  10. 2022ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Modelcited by 34
  11. 2023ProtoSeg: Interpretable Semantic Segmentation with Prototypical Partscited by 33
  12. 2025Language Guided Concept Bottleneck Models for Interpretable Continual Learningcited by 28
38 more
  1. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  2. 2024Interpretable Image Classification with Adaptive Prototype-based Vision Transformerscited by 25
  3. 2023ICICLE: Interpretable Class Incremental Continual Learningcited by 24
  4. 2022Proto2Proto: Can you recognize the car, the way I do?cited by 23
  5. 2023Interpretable Prototype-based Graph Information Bottleneckcited by 23
  6. 2023Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learningcited by 22
  7. 2024On the Concept Trustworthiness in Concept Bottleneck Modelscited by 22
  8. 2023Pixel-Grounded Prototypical Part Networkscited by 18
  9. 2024Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlookscited by 18
  10. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  11. 2024Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?cited by 17
  12. 2023The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifierscited by 15
  13. 2024LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Visioncited by 15
  14. 2025Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Timecited by 13
  15. 2023MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imagingcited by 11
  16. 2023Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognitioncited by 11
  17. 2022Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learningcited by 10
  18. 2023I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnosescited by 10
  19. 2023Q-SENN: Quantized Self-Explaining Neural Networkscited by 9
  20. 2024PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformerscited by 9
  21. 2022ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Predictioncited by 8
  22. 2023Sanity checks for patch visualisation in prototype-based image classificationcited by 8
  23. 2024ProtoS-ViT: Visual foundation models for sparse self-explainable classificationscited by 8
  24. 2023Take 5: Interpretable Image Classification with a Handful of Featurescited by 7
  25. 2023ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]cited by 7
  26. 2024Post-hoc Part-prototype Networkscited by 7
  27. 2024InfoDisent: Explainability of Image Classification Models by Information Disentanglementcited by 7
  28. 2023Prototypical Self-Explainable Models Without Re-trainingcited by 6
  29. 2025SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutionscited by 6
  30. 2025Formal Abductive Latent Explanations for Prototype-Based Networkscited by 5
  31. 2024Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentationcited by 4
  32. 2023Sanity checks and improvements for patch visualisation in prototype-based image classificationcited by 3
  33. 2023Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencodercited by 3
  34. 2023Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retrainingcited by 3
  35. 2023PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humanscited by 3
  36. 2024Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challengescited by 3
  37. 2024Semantic Prototypes: Enhancing Transparency Without Black Boxescited by 3
  38. 2024CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Modelscited by 3

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