mlsanityData to 7 Oct 2026

An Overview of Prototype Formulations for Interpretable Deep Learning

arXiv 2410.08925 ↗First posted 11 Oct 2024cs.LG cs.AI cs.CVcited by 4

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Abstract (arXiv)

Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive analysis of prototype formulations, comparing point-based and probabilistic approaches in both Euclidean and hyperspherical latent spaces. We introduce HyperPG, a probabilistic prototype representation using Gaussian distributions on hyperspheres. Experiments on CUB-200-2011, Stanford Cars, and Oxford Flowers datasets show that hyperspherical prototypes outperform standard Euclidean formulations. Critically, hyperspherical prototypes maintain competitive performance under simplified training schemes, while Euclidean prototypes require extensive hyperparameter tuning.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

23 of 37 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2016Densely Connected Convolutional Networkscited by 6,691
  6. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  7. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  8. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  9. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  10. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  11. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  12. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
11 more
  1. 2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
  2. 2021Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypescited by 120
  3. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  4. 2019Hyperspherical Prototype Networkscited by 85
  5. 2022ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognitioncited by 63
  6. 2023Learning Support and Trivial Prototypes for Interpretable Image Classificationcited by 38
  7. 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizationscited by 36
  8. 2023ProtoSeg: Interpretable Semantic Segmentation with Prototypical Partscited by 33
  9. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  10. 2024MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypescited by 10
  11. 2024ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypescited by 3
  2. 2025Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoningcited by 2
  3. 2025Comprehensive Evaluation of Prototype Neural Networkscited by 1
  4. 2026Beyond L2: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architecturescited by 0