This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
Comprehensive Evaluation of Prototype Neural Networks
Created by
Haebom
Author
Philipp Schlinge, Steffen Meinert, Martin Atzmueller
Outline
This paper provides an in-depth analysis of key prototype models, including ProtoPNet, ProtoPool, and PIPNet. We highlight the importance of prototype models in explainable artificial intelligence (XAI) and interpretable machine learning, and comprehensively evaluate their interpretability using existing and newly proposed metrics. The three models are applied to various datasets (fine-grained classification, non-IID settings, and multi-label classification) to compare and analyze their performance. We also provide an open-source library ( https://github.com/uos-sis/quanproto) for easily adding models and metrics .
Takeaways, Limitations
•
Takeaways:
◦
We present new metrics to comprehensively evaluate the interpretability of prototype models.
◦
We present practical applications by comparing and analyzing the performance of prototype models on various datasets.
◦
Provide open source libraries to increase reproducibility and expandability of research.
•
Limitations:
◦
The types of prototype models included in the analysis may be limited.
◦
Additional validation of the generality and versatility of the proposed new indicators may be necessary.
◦
Further research is needed to determine whether analysis results for a specific dataset can be generalized to other datasets.
