Summary of What Happens to Diffusion Model Likelihood When Your Model Is Conditional?, by Mattias Cross and Anton Ragni
What happens to diffusion model likelihood when your model is conditional?
by Mattias Cross, Anton Ragni
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper explores the properties of Diffusion Models (DMs) and their applications in various tasks. Specifically, it investigates how DMs perform in conditional contexts such as Text-To-Image (TTI) or Text-To-Speech synthesis (TTS). The study reveals that TTS DM likelihoods are surprisingly agnostic to the text input, while TTI likelihood is more expressive but cannot discern confounding prompts. These findings have significant implications for understanding the nature of DM likelihoods and their applications in conditional tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at a special kind of computer model called Diffusion Models (DMs). DMs are good at making realistic images or sounds from text. The scientists wanted to see if these models work differently when they’re given specific texts to generate. They found that sometimes the model doesn’t care what text it’s given, and other times it can make some differences but not always. This is important because it helps us understand how DMs really work. |
Keywords
» Artificial intelligence » Diffusion » Likelihood
