Summary of Editable Concept Bottleneck Models, by Lijie Hu et al.
Editable Concept Bottleneck Models
by Lijie Hu, Chenyang Ren, Zhengyu Hu, Hongbin Lin, Cheng-Long Wang, Hui Xiong, Jingfeng Zhang, Di Wang
First submitted to arxiv on: 24 May 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 The proposed Editable Concept Bottleneck Models (ECBMs) aim to overcome the limitations of traditional Concept Bottleneck Models (CBMs) by enabling efficient editing without retraining from scratch. Specifically, ECBMs support three levels of data removal: concept-label-level, concept-level, and data-level. By leveraging influence functions and closed-form approximations, ECBMs can edit CBMs without requiring retraining, making them more practical for large-scale applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Editable Concept Bottleneck Models (ECBMs) are designed to make it easier to remove or add training data or concepts from trained CBMs without having to start over. This is important because in real-world scenarios, we might need to edit our models for reasons like privacy concerns, mislabeled data, or incorrect concept annotations. The new ECBM approach uses mathematically rigorous methods to make these edits, so we don’t have to retrain the whole model. |
