Summary of An Overview Of Prototype Formulations For Interpretable Deep Learning, by Maximilian Xiling Li et al.
An Overview of Prototype Formulations for Interpretable Deep Learning
by Maximilian Xiling Li, Korbinian Franz Rudolf, Nils Blank, Rudolf Lioutikov
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study explores various prototype formulations in prototypical part networks, which are interpretable alternatives to deep learning models. By examining different prototype representations, this work aims to provide a comprehensive overview of their strengths and limitations. The authors conduct experiments on the CUB-200-2011, Stanford Cars, and Oxford Flowers datasets to demonstrate the effectiveness and versatility of these formulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prototypical part networks are an alternative to deep learning models that provide interpretable results. This study looks at different ways to represent prototypes in these networks. It tests these different approaches on pictures of animals, cars, and flowers to see which ones work best. |
Keywords
* Artificial intelligence * Deep learning
