Summary of Transferring Knowledge From Large Foundation Models to Small Downstream Models, by Shikai Qiu et al.
Transferring Knowledge from Large Foundation Models to Small Downstream Models
by Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper introduces Adaptive Feature Transfer (AFT), a novel method that allows for the efficient transfer of knowledge from large foundation models to smaller, task-specific downstream models. AFT operates on features rather than weights, decoupling the choice of pre-trained model from the downstream model and enabling the adaptive transfer of relevant information. The approach achieves significantly better performance compared to alternatives with similar computational costs across multiple vision, language, and multi-modal datasets. Moreover, AFT reliably translates improvements in pre-trained models into improved downstream performance, even when the downstream model is much smaller. This paper demonstrates the effectiveness of AFT in transferring complementary information learned by multiple pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us share knowledge from big computers to small ones at a lower cost. Right now, we copy weights from these big computers and use them to start learning for a new task. However, this method has some limitations. It doesn’t let us combine the information learned by multiple big computers, which can be useful. To solve this problem, scientists created a new way called Adaptive Feature Transfer (AFT). AFT looks at the features or characteristics of the data rather than just copying weights. This means we can pick the most important features to help with the new task and ignore the rest. The results show that AFT works well on many different types of data, including images, text, and combinations of both. |
Keywords
» Artificial intelligence » Multi modal
