Prototype-Based Models and The Growing Importance of Interpretable AI

Author(s): Shiska Raut

Originally published on Towards AI.

Artificial Intelligence (AI) has transformed how we approach problems in science, industry, and everyday life. Deep learning models now power everything from medical image analysis to autonomous vehicles. While these models deliver remarkable accuracy, they often come with a major drawback: they are black boxes. Their inner workings are so complex that even experts struggle to explain why a particular decision was made.

This lack of transparency is not just an academic issue — in high-stakes fields such as healthcare, law, finance, and biotechnology, understanding the reasoning behind a model’s decision is just as important as the decision itself. Trust, accountability, and fairness all depend on interpretability.

Traditional interpretability methods fall short

To address the opacity of deep neural networks, a variety of post-hoc interpretability techniques have been developed. Methods such as saliency maps[1] and Grad-CAM[2] visualize which parts of an input image most influenced a model’s decision. These techniques are intuitive and have been widely adopted, but they have significant limitations:

• They often highlight “where” a model is looking, but not why that region matters for classification.
• The explanations are not tied to the actual decision-making process of the model, making them unreliable in practice.
• Small perturbations to the input can dramatically change the explanation, reducing trust in their stability.

As a result, researchers have sought approaches that go beyond these approximations and build interpretability into the architecture of the model itself.

Enter prototype-based models

Prototype-based models represent a promising direction in interpretable machine learning. Instead of treating explanations as an afterthought, these models make predictions in a case-based reasoning framework:

• The model learns prototypes — small patches or exemplars from the training data that capture distinctive features of each class.
• Predictions are made by comparing parts of a new input to these prototypes.
• Explanations come naturally: the model can say, “This image looks like that prototypical example,” mimicking the way humans justify decisions.

This approach offers faithful explanations because the prototypes are directly tied to the model’s internal reasoning, unlike saliency maps or heatmaps that are generated after the fact.

Key prototype-based architectures

The field of prototype networks has evolved quickly, with several notable architectures addressing different aspects of interpretability

ProtoPNet: The First Step Toward Prototype-Based Interpretability

The first major breakthrough in prototype-based interpretability was ProtoPNet [4]. This architecture was the foundation for much of the subsequent work in the field.

ProtoPNet builds on a standard convolutional neural network by adding a prototype layer. Instead of making predictions directly from feature maps, the model learns a fixed number of part-prototypes for each class. These prototypes correspond to meaningful image patches (e.g., a bird’s wing or a car’s headlights) and are compared to regions of a new input image using $L_2$ distance. A prediction is then made based on how strongly the input matches the learned prototypes.

To generate explanations, ProtoPNet projects each prototype back onto the closest patch from the training set. This allows the model to justify its decision in human terms: “This part of the image looks like that part of a training example.”

Why it mattered

This case-based reasoning marked a major shift in interpretable deep learning. Instead of relying on heatmaps or post-hoc approximations, ProtoPNet tied explanations directly to the way the model makes predictions. The result was a framework that was not only accurate but also transparent and intuitive. The key note here is that ProtopNet uses latent space representations of actual training images as prototypes in a step called ‘prototype projection’. This allows the user to visualize the prototypical parts in image space and verify whether the prototype is truly representative of the visual feature.

Limitations

Despite its novelty, ProtoPNet also exposed important challenges that shaped later research:

• Prototype inconsistency: The same prototype could activate on different object parts across images, reducing explanation reliability.
• Prototype instability: Small input perturbations (like noise) could cause prototypes to shift activations, undermining robustness.
• Poor diversity: Multiple prototypes often collapsed onto the same visual feature, limiting the richness of explanations.

These issues highlighted the need for more reliable, stable, and diverse prototypes — problems that inspired many follow-up models such as TesNet[7], Deformable ProtoPNet[6] and ProtoPAligned[5].

Other notable architectures

Building on ProtoPNet, several extensions have been proposed to address limitations in prototype diversity and reliability.

TesNet introduced cosine similarity in place of Euclidean distance and incorporated an orthogonality loss, encouraging prototypes to be both more distinct and more consistent.

Following this direction, models such as Deformable ProtoPNet and ProtoPool further adopted cosine similarity and orthogonality loss to enhance prototype diversity and improve overall performance.

ProtoPAligned shifted the focus more explicitly toward interpretability by adding architectural modules such as Shallow–Deep Feature Alignment and Score Aggregation. It also formally introduced the consistency and stability scores, establishing quantitative metrics for evaluating prototype reliability and marking an important move away from purely qualitative inspection.

Why prototype-based models matter

Prototype networks stand out because they provide case-based explanations directly tied to the decision-making process. This is particularly powerful in fine-grained tasks such as distinguishing bird species, medical diagnoses, or defect detection, where small, localized differences matter.

By grounding predictions in real examples, these models offer:

• Faithfulness: Explanations reflect how the model actually makes decisions.
• Transparency: Users can see which parts of an input are matched with meaningful prototypes.
• Trustworthiness: Stable and consistent prototypes help build confidence in the model’s reasoning.

At the same time, challenges remain. Ensuring prototype diversity, robustness to noise, and scalability to large datasets are ongoing research questions. Nonetheless, the trajectory of work in this field demonstrates an encouraging trend: interpretability is being treated as a first-class goal, not an afterthought.

Closing thoughts

Prototype-based models are reshaping how we think about interpretable machine learning. From the early breakthroughs of ProtoPNet to the more advanced formulations of ProtoPAligned and beyond, these models provide a blueprint for designing systems that are not only accurate but also interpretable.

As AI continues to move into critical domains, the importance of such approaches cannot be overstated. While no single model has solved interpretability, prototype-based networks are a step toward bridging the gap between black-box performance and human-centered transparency — a step that may ultimately make AI more accountable, trustworthy, and useful in the real world.

References:

1. K. Simonyan, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” arXiv preprint arXiv:1312.6034, 2013

2. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, pp. 618–626, 2017.

3. T. Laugel, M.-J. Lesot, C. Marsala, X. Renard, and M. Detyniecki, “The dangers of post-hoc interpretability: Unjustified counterfactual explanations,” arXiv preprint arXiv:1907.09294, 2019.

4. C. Chen, O. Li, C. Tao, A. J. Barnett, J. Su, and C. Rudin, “This looks like that: Deep learning for interpretable image recognition,” 2018.

5. Q. Huang, M. Xue, W. Huang, H. Zhang, J. Song, Y. Jing, and M. Song, “Evaluation and improvement of interpretability for self-explainable part-prototype networks,” tech. rep., 2023.

6. J. Wang, H. Liu, X. Wang, and L. Jing, “Interpretable image recognition by constructing transparent embedding space,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 895–904, 2021.

7. J. Donnelly, A. J. Barnett, and C. Chen, “Deformable protopnet: An interpretable image classifier using deformable prototypes,” 2024.

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Published via Towards AI