mlsanityData to 14 Oct 2026

On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis

arXiv 2310.06966 ↗First posted 10 Oct 2023cs.CV cs.AI cs.HCcited by 11

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Abstract (arXiv)

Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective. The proposed framework consists of three actionable metrics and experiments. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

14 of 36 references.

References · 14

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 737
  4. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  5. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 214
  6. 2021Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypescited by 120
  7. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  8. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  9. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  10. 2018How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanationcited by 72
  11. 2021This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networkscited by 65
  12. 2022Concept-level Debugging of Part-Prototype Networkscited by 52
2 more
  1. 2022Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networkscited by 37
  2. 2018Human-in-the-Loop Interpretability Priorcited by 36

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 2.

11 papers.

  1. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  2. 2024This actually looks like that: Proto-BagNets for local and global interpretability-by-designcited by 7
  3. 2025Formal Abductive Latent Explanations for Prototype-Based Networkscited by 5
  4. 2024Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentationcited by 4
  5. 2023Robust Text Classification: Analyzing Prototype-Based Networkscited by 3
  6. 2024This Probably Looks Exactly Like That: An Invertible Prototypical Networkcited by 3
  7. 2025This looks like what? Challenges and Future Research Directions for Part-Prototype Modelscited by 3
  8. 2023Model-agnostic Body Part Relevance Assessment for Pedestrian Detectioncited by 0
  9. 2025Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Trainingcited by 0
  10. 2026Metonymy in vision models undermines attention-based interpretabilitycited by 0
  11. 2026OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Modelscited by 0