Summary of Analyzing and Mitigating Model Collapse in Rectified Flow Models, by Huminhao Zhu et al.
Analyzing and Mitigating Model Collapse in Rectified Flow Models
by Huminhao Zhu, Fangyikang Wang, Tianyu Ding, Qing Qu, Zhihui Zhu
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper examines the reliability of synthetic data for deep learning models, particularly in the context of Rectified Flow (Reflow) models and Denoising Autoencoders (DAEs). The authors investigate the phenomenon of model collapse (MC), where performance degrades over time when training on self-generated samples. They provide a theoretical analysis of reflow methods, highlighting potential issues with iterative training on synthetic data. The study demonstrates that incorporating real data can prevent MC during recursive DAE training, and proposes novel approaches to mitigate MC, such as Real-data Augmented Reflow (RA Reflow). Empirical evaluations confirm the effectiveness of RA Reflow in preserving high-quality sample generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using fake data for machine learning models. Researchers are making a lot of fake content on the internet, which can be used to train machines. But sometimes, when machines use this fake data to learn, they get worse over time. This problem is called model collapse. The authors studied how this happens and found that it’s because machines are using too much fake data and not enough real data. They came up with some new ways to fix this problem, which involve mixing in a little bit of real data when training the machine. These new methods work well and can help keep the machine from getting worse over time. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Synthetic data
