Summary of Heuristic-free Multi-teacher Learning, by Huy Thong Nguyen et al.
Heuristic-Free Multi-Teacher Learning
by Huy Thong Nguyen, En-Hung Chu, Lenord Melvix, Jazon Jiao, Chunglin Wen, Benjamin Louie
First submitted to arxiv on: 19 Nov 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 In this paper, researchers introduce Teacher2Task, a framework for multi-teacher learning that eliminates the need for manual aggregation heuristics. Traditional methods rely on these heuristics to combine predictions from multiple teachers, leading to sub-optimal aggregated labels and propagated errors. Teacher2Task addresses these limitations by using teacher-specific input tokens and reformulating the training process. Instead of relying on aggregated labels, the framework transforms the training data into N+1 distinct tasks: N auxiliary tasks that predict the labeling styles of individual teachers, and one primary task that focuses on ground truth labels. This approach, drawing from multiple learning paradigms, demonstrates strong empirical results across various architectures, modalities, and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Teacher2Task is a new way to learn from many teachers without needing extra steps. Right now, when we combine what different teachers predict, it’s not perfect because we have to make choices along the way. Teacher2Task gets rid of these choices by turning the training data into multiple small tasks that help us understand each teacher’s style and one main task that focuses on the real answers. This makes it better at learning from many teachers and works well with different types of information. |
