Summary of Improving Intervention Efficacy Via Concept Realignment in Concept Bottleneck Models, by Nishad Singhi et al.
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
by Nishad Singhi, Jae Myung Kim, Karsten Roth, Zeynep Akata
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 new concept-based model for image classification, called Concept Bottleneck Models (CBMs), allows for interpretable decision-making by grounding the model’s outputs on human-understandable concepts. This design enables human interventions to modify potentially misaligned concept choices and influence the model’s decisions. However, existing approaches require numerous human interventions per image, which can be impractical in scenarios where obtaining human feedback is costly. To address this issue, a trainable concept intervention realignment module is introduced, leveraging concept relations to realign concept assignments post-intervention. This module significantly improves intervention efficacy, reducing the number of interventions needed to reach target classification performance or concept prediction accuracy. The module also easily integrates into existing concept-based architectures without requiring changes to the models themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of image classification model helps people understand how it makes decisions by using simple concepts. This design lets experts correct any mistakes and make the model better. But, right now, it takes a lot of expert help per picture, which can be expensive. To fix this problem, a special module is created that helps align the concepts in the model after they’re corrected. This module makes it easier to get good results with less expert help needed. It also works well with other similar models. |
Keywords
» Artificial intelligence » Classification » Grounding » Image classification
