Summary of Can We Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?, by Jack Furby et al.
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
by Jack Furby, Daniel Cunnington, Dave Braines, Alun Preece
First submitted to arxiv on: 1 Feb 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 This paper explores the idea that Concept Bottleneck Models (CBMs) can be trained to use semantically meaningful input features when predicting concepts. The authors propose that current literature suggests that CBMs often rely on irrelevant input features, and hypothesise that this occurs due to inaccurate concept annotations or unclear relationships between input features and concepts. To validate their hypothesis, the researchers demonstrate that CBMs can learn to map concepts to relevant input features by utilising datasets with clear links between input features and desired concept predictions. This is achieved by ensuring multiple concepts do not co-occur, providing a training signal for the CBM to distinguish relevant input features. The authors test their approach on both synthetic and real-world image datasets, showing that under the correct conditions, CBMs can learn to attribute semantically meaningful input features to concept predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models more understandable by training them to use meaningful information from images or other data. Right now, these models often rely on irrelevant details instead of focusing on what’s important. The researchers think this happens because the labels used to train the model are inaccurate or unclear. They tested their idea by using datasets where the relationship between input features and concepts is clear. This helped the model learn to focus on relevant details, making it more accurate and easier to understand. |
Keywords
* Artificial intelligence * Machine learning
