Summary of Transfer Learning For Diffusion Models, by Yidong Ouyang et al.
Transfer Learning for Diffusion Models
by Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng
First submitted to arxiv on: 27 May 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 The proposed Transfer Guided Diffusion Process (TGDP) is a novel approach that transfers knowledge from existing pre-trained diffusion models to specific target domains with limited data, leveraging finetuning and regularization methods. The optimal diffusion model for the target domain integrates pre-trained diffusion models on the source domain with additional guidance from a domain classifier. A conditional version of TGDP is also introduced for modeling the joint distribution of data and its corresponding labels, along with two additional regularization terms to enhance performance. Experimental results demonstrate the effectiveness of TGDP on both simulated and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to make artificial intelligence models better at learning from small amounts of data. The method, called Transfer Guided Diffusion Process (TGDP), helps existing AI models learn from new data that’s different from what they’ve seen before. This is important because it can be hard or expensive to collect lots of training data in real-life situations. The researchers show that TGDP works well on both made-up and real-world datasets. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Regularization
