mlsanityData to 14 Oct 2026

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes

arXiv 2111.15000 ↗First posted 29 Nov 2021cs.CV cs.AI cs.LGcited by 120

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Abstract (arXiv)

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of "this looks like that." However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36/Deformable-ProtoPNet.

Key facts. Cited by 120 papers in the map (data through 14 Oct 2026). Builds on 35 of its 60 references that are in the map. First posted on arXiv 29 Nov 2021.

Builds on

35 of 60 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
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  10. 2017Deformable Convolutional Networkscited by 1,488
  11. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  12. 2015Spatial Transformer Networkscited by 1,429
23 more
  1. 2017SmoothGrad: removing noise by adding noisecited by 1,109
  2. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  3. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  4. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 737
  5. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 737
  6. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 670
  7. 2015Understanding Neural Networks Through Deep Visualizationcited by 334
  8. 2016Large-Margin Softmax Loss for Convolutional Neural Networkscited by 296
  9. 2018Additive Margin Softmax for Face Verificationcited by 278
  10. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  11. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
  12. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 214
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  14. 2019Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognitioncited by 95
  15. 2020Interpretable and Accurate Fine-grained Recognition via Region Groupingcited by 75
  16. 2021IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammographycited by 72
  17. 2020This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognitioncited by 50
  18. 2014Deformable Part Models are Convolutional Neural Networkscited by 35
  19. 2020Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filterscited by 26
  20. 2017Active Convolution: Learning the Shape of Convolution for Image Classificationcited by 24
  21. 2014DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detectioncited by 19
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  23. 2016Fully Convolutional Attention Networks for Fine-Grained Recognitioncited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 33; 2024: 32; 2025: 28; 2026: 16.

120 papers.

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  1. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  2. 2023Concept-based Explainable Artificial Intelligence: A Surveycited by 98
  3. 2022ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognitioncited by 63
  4. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  5. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 39
  6. 2023Learning Support and Trivial Prototypes for Interpretable Image Classificationcited by 38
  7. 2022Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networkscited by 37
  8. 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizationscited by 36
  9. 2023ProtoSeg: Interpretable Semantic Segmentation with Prototypical Partscited by 33
  10. 2023ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classificationcited by 33
  11. 2022Explainable Deep Learning Methods in Medical Image Classification: A Surveycited by 32
  12. 2023Text-to-Image Models for Counterfactual Explanations: a Black-Box Approachcited by 28
38 more
  1. 2025Language Guided Concept Bottleneck Models for Interpretable Continual Learningcited by 28
  2. 2025Can Masked Autoencoders Also Listen to Birds?cited by 26
  3. 2025Perch 2.0: The Bittern Lesson for Bioacousticscited by 26
  4. 2023Variational Information Pursuit for Interpretable Predictionscited by 25
  5. 2024Interpretable Image Classification with Adaptive Prototype-based Vision Transformerscited by 25
  6. 2025Interpretable and Testable Vision Features via Sparse Autoencoderscited by 25
  7. 2023ICICLE: Interpretable Class Incremental Continual Learningcited by 24
  8. 2024Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discoverycited by 21
  9. 2024Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlookscited by 18
  10. 2024DCBM: Data-Efficient Visual Concept Bottleneck Modelscited by 16
  11. 2023A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysiscited by 15
  12. 2024LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Visioncited by 15
  13. 2025Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Timecited by 13
  14. 2024B-cosification: Transforming Deep Neural Networks to be Inherently Interpretablecited by 12
  15. 2023MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imagingcited by 11
  16. 2023On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysiscited by 11
  17. 2023Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognitioncited by 11
  18. 2024Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Surveycited by 11
  19. 2024This Looks Better than That: Better Interpretable Models with ProtoPNeXtcited by 11
  20. 2025Interpretable Image Classification via Non-parametric Part Prototype Learningcited by 11
  21. 2022Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learningcited by 10
  22. 2024MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypescited by 10
  23. 2024AudioProtoPNet: An interpretable deep learning model for bird sound classificationcited by 10
  24. 2022Causality-Inspired Taxonomy for Explainable Artificial Intelligencecited by 9
  25. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  26. 2024Intrinsic User-Centric Interpretability through Global Mixture of Expertscited by 9
  27. 2023Sanity checks for patch visualisation in prototype-based image classificationcited by 8
  28. 2024ProtoS-ViT: Visual foundation models for sparse self-explainable classificationscited by 8
  29. 2024InfoDisent: Explainability of Image Classification Models by Information Disentanglementcited by 8
  30. 2023Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Diseasecited by 7
  31. 2023Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspacecited by 7
  32. 20233VL: Using Trees to Improve Vision-Language Models' Interpretabilitycited by 7
  33. 2024PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneckcited by 7
  34. 2024Post-hoc Part-prototype Networkscited by 7
  35. 2025Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classificationcited by 7
  36. 2023Prototypical Self-Explainable Models Without Re-trainingcited by 6
  37. 2023Hierarchical Explanations for Video Action Recognitioncited by 5
  38. 2023ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinementcited by 5

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