Summary of Heat Death Of Generative Models in Closed-loop Learning, by Matteo Marchi et al.
Heat Death of Generative Models in Closed-Loop Learning
by Matteo Marchi, Stefano Soatto, Pratik Chaudhari, Paulo Tabuada
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 In this paper, researchers investigate how generative machine learning models respond when they’re trained on data generated by themselves and other models. This is a question about the stability of these training processes, considering that data generated by models is now being shared publicly through the web. Large Language Models (LLMs) for text and diffusion models for image generation are specifically examined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models work when they’re trained on data they helped create themselves. The researchers want to know if this process stays stable or breaks down over time. They use big language models that can understand and generate text, as well as image models that can create realistic pictures. |
Keywords
* Artificial intelligence * Image generation * Machine learning
