Summary of Co-supervised Learning: Improving Weak-to-strong Generalization with Hierarchical Mixture Of Experts, by Yuejiang Liu et al.
Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts
by Yuejiang Liu, Alexandre Alahi
First submitted to arxiv on: 23 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 The proposed method harnesses a diverse set of specialized teachers to collectively supervise a strong student model, addressing the challenge of weak-to-strong generalization in the presence of large capability gaps. The approach resembles hierarchical mixture of experts with two components: progressive alternating training and teacher assignment, leveraging the growth of the strong student; and conservative enforcement of teacher-student consistency, rejecting potential annotation noises. This method is validated through visual recognition tasks on OpenAI’s weak-to-strong benchmark and additional multi-domain datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to help a super-smart AI model learn from many teachers with different specialties. Instead of having one general teacher, it uses multiple specialized teachers who work together to supervise the student. The approach is like a hierarchical system where the teachers are mixed and matched in different combinations. This helps the strong student model learn more effectively by rejecting noisy or incorrect information. The method is tested on some visual recognition tasks and shows promising results. |
Keywords
* Artificial intelligence * Generalization * Mixture of experts * Student model
