A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker
Abstract
Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as lower performance. This led to the development of the socalled deep Prototype-Based Networks (PBNs), also known as prototypical parts models. In this work, we analyze these models with respect to different properties, including interpretability. In particular, we focus on the Classification-by-Components (CBC) approach, which uses a probabilistic model to ensure interpretability and can be used as a shallow or deep architecture. We show that this model has several shortcomings, like creating contradicting explanations. Based on these findings, we propose an extension of CBC that solves these issues. Moreover, we prove that this extension has robustness guarantees and derive a loss that optimizes robustness. Additionally, our analysis shows that most (deep) PBNs are related to (deep) RBF classifiers, which implies that our robustness guarantees generalize to shallow RBF classifiers. The empirical evaluation demonstrates that our deep PBN yields state-of-the-art classification accuracy on different benchmarks while resolving the interpretability shortcomings of other approaches. Further, our shallow PBN variant outperforms other shallow PBNs while being inherently interpretable and exhibiting provable robustness guarantees. Backbone Latent Proto. Similarity Linear Layer Constraints Single Loss Main Contribution LeNet5 single yes RBF none no CNN with RBF head ProtoPNet* single yes* RBF (log) l 1 reg. no (deep) NN with prototype classification head CBC* Siamese* no RBF or ReLU-cosine probabilistic yes negative/positive/indefinite reasoning Hier. ProtoPNet single yes* RBF (log) l 1 reg. no hierarchical classification ProtoAttend* Siamese no relational attention none no attention for prototype selection ProtoTree single yes* RBF (soft) tree* yes tree upon similarities ProtoPShare single yes* RBF (log) l 1 reg. no prototype sharing between classes TesNet single yes* dot-product l 1 reg. no orthogonal prototypes
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 3eb8725a-dc67-47b3-978b-ac1c35848726Cited by top-tier papers2
- ProtoPairNet: Interpretable Regression through Prototypical Pair ReasoningRose Gurung, Ronilo J. Ragodos, Chiyu Ma, Tong Wang et al.NeurIPS 2025 · 1 citation
- Neural Additive Adapters for Interpretable Nutrition PredictionVitalii Emelianov, Niki MartinelACM MM 2025
Builds on12
- A ConvNet for the 2020sZhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer et al.CVPR 2022 · 6,782 citations
- Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacksFrancesco Croce, Matthias HeinICML 2020 · 2,337 citations
- Uncertainty Estimation Using a Single Deep Deterministic Neural NetworkJoost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin GalICML 2020 · 529 citations
- Interpretable Image Recognition by Constructing Transparent Embedding SpaceJiaqi Wang, Huafeng Liu, Xinyue Wang, Liping JingICCV 2021 · 149 citations
- Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable PrototypesJon Donnelly, Alina Jade Barnett, Chaofan ChenCVPR 2022 · 101 citations
Related papers
- Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype NetworksQihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang et al.ICCV 2023 · 47 citations
- Interpretable Image Classification via Non-parametric Part Prototype LearningZhijie Zhu, Lei Fan, Maurice Pagnucco, Yang SongCVPR 2025
- This Looks Like Those: Illuminating Prototypical Concepts Using Multiple VisualizationsChiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia RudinNeurIPS 2023 · 53 citations
- This Looks Like It Rather Than That: ProtoKNN For Similarity-Based ClassifiersYuki Ukai, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu FujiyoshiICLR 2023
- PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image ClassificationMeike Nauta, Jörg Schlötterer, Maurice van Keulen, Christin SeifertCVPR 2023
