Summary of Explanation Bottleneck Models, by Shin’ya Yamaguchi and Kosuke Nishida
Explanation Bottleneck Models
by Shin’ya Yamaguchi, Kosuke Nishida
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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 This paper introduces a novel interpretable deep neural network called explanation bottleneck models (XBMs), which generate text explanations from input data without relying on pre-defined concepts. XBMs leverage pre-trained vision-language encoder-decoder models to predict a final task prediction based on the generated explanation. The authors train XBMs through a target task loss with regularization, penalizing the explanation decoder via distillation from a frozen pre-trained decoder. Experimental results, including comparisons to state-of-the-art concept bottleneck models, demonstrate that XBMs provide accurate and fluent natural language explanations without requiring pre-defined concepts. This approach has implications for improving model interpretability in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to explain how computers work on data without needing special “concept sets”. They call this new method “explanation bottleneck models” (XBMs). XBMs can generate text explanations from any input data, without relying on specific ideas or concepts. The authors tested their approach and found that it works well, producing accurate and easy-to-understand explanations. This breakthrough could help improve how we understand complex computer models and make them more transparent. |
Keywords
» Artificial intelligence » Decoder » Distillation » Encoder decoder » Neural network » Regularization
