Summary of Model Collapse in the Self-consuming Chain Of Diffusion Finetuning: a Novel Perspective From Quantitative Trait Modeling, by Youngseok Yoon et al.
Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling
by Youngseok Yoon, Dainong Hu, Iain Weissburg, Yao Qin, Haewon Jeong
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 paper investigates the phenomenon of “model collapse” in generative models, where their outputs become indistinguishable from real data but suffer from significant degradation in performance when used iteratively. A pretrained text-to-image diffusion model is finetuned using synthetic images generated from a previous iteration, a process called the “Chain of Diffusion.” The authors demonstrate the decline in image quality and identify the key factor driving this decline through empirical investigations. They then propose a novel theoretical analysis based on quantitative trait modeling, which aligns with empirical observations. Finally, they introduce Reusable Diffusion Finetuning (ReDiFine), a simple strategy that mitigates model collapse without requiring hyperparameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence models can create fake images and videos that are hard to tell apart from real ones. This is great for making new content quickly, but it has a problem called “model collapse.” When you use these models over and over again, they start to make worse and worse fake images. The researchers looked into why this happens and found that the main reason is that the model starts to get stuck in a loop of creating more fake images instead of learning from new data. They then came up with an idea called “ReDiFine” that helps prevent this problem by introducing some random changes to the model’s training process. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Hyperparameter
