Summary of Enhancing Accuracy in Generative Models Via Knowledge Transfer, by Xinyu Tian and Xiaotong Shen
Enhancing Accuracy in Generative Models via Knowledge Transfer
by Xinyu Tian, Xiaotong Shen
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the accuracy of generative models by fine-tuning pre-trained models from one task to another, leveraging “Shared Embedding” concepts to bridge gaps between tasks. It proposes a novel framework for transfer learning using distribution metrics like Kullback-Leibler divergence. The framework highlights the importance of identifying shared structures and transferring knowledge effectively from source to target learning. To demonstrate practical utility, the paper explores theoretical implications for diffusion and normalizing flow generative models. Results show enhanced performance in both models over non-transfer counterparts, indicating advancements in diffusion models and new insights into normalizing flows. This work contributes significantly to boosting generation capabilities of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good generative models are at making things up. It tries to make one model better by using another model as a guide. The idea is that even if the tasks are different, there might be some shared ideas or patterns that can help. The paper shows that this works and makes two types of generative models do a better job. |
Keywords
» Artificial intelligence » Boosting » Diffusion » Embedding » Fine tuning » Transfer learning
