Summary of Conceptual Learning Via Embedding Approximations For Reinforcing Interpretability and Transparency, by Maor Dikter et al.
Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency
by Maor Dikter, Tsachi Blau, Chaim Baskin
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 introduces CLEAR, a framework for constructing concept bottleneck models (CBMs) that enable more accurate and interpretable image classification. CBMs rely on predefined textual descriptions, or concepts, to inform their decision-making process. The authors propose an approach called Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency (CLEAR), which learns the scores associated with the joint distribution of images and concepts using score matching and Langevin sampling. A concept selection process is then employed to optimize the similarity between the learned embeddings and predefined ones. This derived bottleneck offers insights into the CBM’s decision-making process, enabling more comprehensive interpretations. The authors demonstrate state-of-the-art performance on various benchmarks and provide their code for experiments at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer models better at understanding images. It’s called CLEAR, and it helps us understand why the model made certain decisions. Right now, these models are good at classifying images, but they don’t tell us much about how they got there. The researchers in this study want to change that by learning more about what makes each image unique. They use a special combination of math and computer programming to create a new type of model that can explain its decisions. This model is really good at classifying images, and it’s the first one that can give us insight into how it works. The researchers share their code so others can try it out. |
Keywords
» Artificial intelligence » Embedding » Image classification
