Summary of Concept Bottleneck Models Without Predefined Concepts, by Simon Schrodi et al.
Concept Bottleneck Models Without Predefined Concepts
by Simon Schrodi, Julian Schur, Max Argus, Thomas Brox
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper introduces a novel approach for converting black-box models into interpretable concept-based models without relying on human-annotated concepts or predefined sets of concepts. By leveraging unsupervised concept discovery, the method automatically extracts relevant concepts from the input data and applies an input-dependent concept selection mechanism to reduce the number of concepts used in classification. The results show that this approach improves downstream performance and narrows the gap with black-box models while using fewer concepts. Additionally, the paper demonstrates how large vision-language models can intervene on model weights to correct errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to make complicated AI models more understandable by identifying important ideas or concepts within the data without needing human help. They used an unsupervised approach to discover these concepts and then chose which ones are most relevant for each classification task. This new method performed better than previous attempts and can even correct its own mistakes with the help of larger AI models. |
Keywords
* Artificial intelligence * Classification * Unsupervised
