Summary of Incremental Residual Concept Bottleneck Models, by Chenming Shang et al.
Incremental Residual Concept Bottleneck Models
by Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, Yuwang Wang
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Concept Bottleneck Models (CBMs) bridge the gap between deep neural networks’ visual representations and interpretable concepts. By leveraging multimodal pre-trained models, CBMs can generate concept bottlenecks without manual annotations. Recent research has focused on establishing a comprehensive concept bank, but constructing one through humans or large language models is challenging. To address this limitation, we propose the Incremental Residual Concept Bottleneck Model (Res-CBM), which uses optimizable vectors to complete missing concepts and an incremental concept discovery module to convert unclear meanings into potential concepts. Our approach can be applied to any user-defined concept bank as a post-hoc processing method. We also introduce the Concept Utilization Efficiency (CUE) metric to measure CBMs’ descriptive efficiency. Experimental results show that Res-CBM outperforms current state-of-the-art methods in terms of accuracy and efficiency, achieving comparable performance to black-box models across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on making deep learning models more understandable by mapping their “thoughts” onto simple ideas. They’ve developed a way to do this using special pre-trained models that can match what these neural networks see with words we understand. However, building a big collection of these simple ideas is hard because it requires a lot of work and expertise. To solve this problem, scientists have created a new model called the Incremental Residual Concept Bottleneck Model (Res-CBM) that can fill in gaps in this collection using special vectors. This new model can be used with any set of simple ideas we define, making it more efficient and accurate than previous models. |
Keywords
* Artificial intelligence * Deep learning
