The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
Open accessAbstract
Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design. Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this learned prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it’s a hummingbird! We tune the accuracy-interpretability trade-off using ensemble methods, pruning and binarizing. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 learned prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200-2011 and Stanford Cars data sets. Code is available at github.com/M-Nauta/ProtoTree.
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“Hence, no manual formulation of prototypes or concepts is required.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [62]
In the citing paper
“In this sense, ProtoTree (Neural Prototype Trees) [62] provides a local and global explanation utilizing a decision tree that is explainable by design.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [62]
In the citing paper
“Its hierarchical structure is intended to increase interpretability and lead to more insights regarding clusters in the data by exploiting the positive and the negative reasoning process.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [62]
In the citing paper
“No manual formulation of prototypes/ concepts is required”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [62]
In the citing paper
“Possibility of ambiguous result”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [62]
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Citations
Cites
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Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
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Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
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Research works printed but not linked: 2
- kindermans, 2019, The (Un)reliability of Saliency Methods, 267
- wah, 2011, The Caltech-UCSD Birds-200 - 2011 Dataset, Technical Report CNS-TR-2011-001
Cited by
Sorted by citations
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., & Seifert, C. (2023) · ACM Computing Surveys · doi:10.1145/3583558 · Cited by 590
Self-citationBenchmarking and survey of explanation methods for black box models
Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 410
Decision trees: from efficient prediction to responsible AI
Blockeel, H., Devos, L., Frénay, B., Nanfack, G., & Nijssen, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1124553 · Cited by 301
Metadata
- Authors
- Nauta, M., van Bree, R., & Seifert, C.
- Affiliations
- University of Twente (NL)
- University of Duisburg-Essen (DE)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 14928-14938
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3108752471
- Cited by
- 269
- References
- 64
- Citations by year
- 2026: 28 · 2025: 69 · 2024: 69 · 2023: 70 · 2022: 24 · 2021: 8
- FWCI
- 19.75
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- prototype learning, decision tree, explainable deep learning, fine-grained image classification, explainable AI, local explanations, accuracy-interpretability trade-off, tree pruning, ensemble methods, binarization, bird species classification, CUB-200-2011, Stanford Cars
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