Summary of Embedding Compression For Teacher-to-student Knowledge Transfer, by Yiwei Ding and Alexander Lerch
Embedding Compression for Teacher-to-Student Knowledge Transfer
by Yiwei Ding, Alexander Lerch
First submitted to arxiv on: 9 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 AI research paper proposes a new approach to using embeddings as teachers in knowledge distillation methods. The authors acknowledge that previous work has ignored the fact that teacher embeddings can contain irrelevant information for the target task. To address this issue, they introduce an embedding compression module with a trainable transformation to obtain a compact teacher embedding. The results show improved classification performance, especially when using unsupervised teacher embeddings. Furthermore, student models trained with these compressed embeddings demonstrate stronger generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us understand how we can use embeddings as teachers in learning new things. Right now, most teaching methods require the teacher and student to learn the same thing. But what if the teacher learned something completely different? This could cause problems because the teacher might be sharing information that’s not useful for the student. To solve this issue, the authors suggest a way to shrink the teacher’s knowledge into a more focused version that only shares relevant information. This new approach improves how well students learn and helps them apply what they’ve learned in new situations. |
Keywords
* Artificial intelligence * Classification * Embedding * Knowledge distillation * Unsupervised
