Summary of Covariance-adaptive Sequential Black-box Optimization For Diffusion Targeted Generation, by Yueming Lyu et al.
Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation
by Yueming Lyu, Kim Yong Tan, Yew Soon Ong, Ivor W. Tsang
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 The paper presents a novel approach to fine-tuning diffusion models for targeted content generation using only black-box target scores. The authors formulate this problem as a sequential optimization task, proposing a covariance-adaptive algorithm to optimize cumulative scores under unknown transition dynamics. They prove a convergence rate of O(d^2/√T) for cumulative convex functions and demonstrate the effectiveness of their method on numerical test problems and 3D-molecule generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer models called diffusion models to create new content, like images or text, based on what users prefer. The challenge is that we don’t know exactly how to make these models do what the user wants, but all we have is a score that tells us how good the generated content is. To solve this problem, the authors come up with a way to fine-tune these models using an optimization algorithm that adapts to the task at hand. They test their approach on some examples and show that it works better than other methods. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Optimization
