Summary of End-to-end Training Induces Information Bottleneck Through Layer-role Differentiation: a Comparative Analysis with Layer-wise Training, by Keitaro Sakamoto et al.
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training
by Keitaro Sakamoto, Issei Sato
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 why end-to-end (E2E) training, which optimizes entire models through error backpropagation, achieves superior performance in deep learning. Despite its high performance, E2E training faces memory consumption, parallel computing, and functionality issues. Alternative methods have been proposed to overcome these difficulties, but none match E2E’s performance, making them impractical. The paper examines the differences between trained model properties beyond performance gaps by comparing E2E with layer-wise training, a non-E2E method that sets local errors. It analyzes information plane dynamics of intermediate representations based on the Hilbert-Schmidt independence criterion (HSIC) and reveals E2E’s ability to exhibit different information dynamics across layers. This layer-role differentiation leads to final representation following the information bottleneck principle, suggesting cooperative interactions between layers when analyzing deep learning’s information bottleneck. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how a special way of training AI models called end-to-end (E2E) helps them learn better. E2E has some problems, like using too much memory or not working well with many computers at once. People have tried to fix these issues, but none of their solutions work as well as E2E. The paper looks at what makes E2E so good by comparing it to another way of training called layer-wise training. It also studies how information flows through the model and finds that E2E is better at handling different types of information. This helps us understand why deep learning models can be so good. |
Keywords
* Artificial intelligence * Backpropagation * Deep learning
