Summary of Generalizing Teacher Networks For Effective Knowledge Distillation Across Student Architectures, by Kuluhan Binici et al.
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
by Kuluhan Binici, Weiming Wu, Tulika Mitra
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A machine learning education framework is proposed, which enables efficient knowledge distillation (KD) by creating a generic teacher model that can effectively transfer knowledge to any student model from a given pool of architectures. The generic teacher network (GTN) is trained using a KD-aware approach that conditions the teacher to align with the capacities of various student architectures sampled from the weight-sharing supernet. Experimental results demonstrate improved overall KD effectiveness and amortized additional training costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to teach machines how to learn from each other is developed. This method, called generic teacher network (GTN), allows a teacher model to share its knowledge with many different student models, even if they have very different architectures. The GTN is trained to work well with all these different students, making it more efficient and effective than previous methods. By doing so, the GTN can be used in various situations where machines need to learn from each other, such as when deploying them on different devices or hardware. |
Keywords
* Artificial intelligence * Knowledge distillation * Machine learning * Student model * Teacher model
