Summary of Active Diffusion Subsampling, by Oisin Nolan et al.
Active Diffusion Subsampling
by Oisin Nolan, Tristan S. W. Stevens, Wessel L. van Nierop, Ruud J. G. van Sloun
First submitted to arxiv on: 20 Jun 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 The proposed Active Diffusion Subsampling (ADS) method leverages guided diffusion models to actively subsample high-dimensional signals, minimizing uncertainty about the true signal of interest. By tracking a distribution of beliefs over the true state throughout the reverse diffusion process, ADS progressively decreases its uncertainty by choosing measurement locations with maximum expected entropy. This approach is interpretable and transparent, unlike existing black-box policies. ADS can be applied using pre-trained diffusion models for any subsampling rate, without requiring task-specific retraining. The method outperforms fixed sampling strategies and is shown to perform competitively with supervised methods in Magnetic Resonance Imaging acceleration tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active Diffusion Subsampling (ADS) is a new way to reduce the cost of collecting data by selecting the most important measurements. It uses a special type of model called guided diffusion that can generate high-quality predictions about unknown data. ADS works by using these models to track its uncertainty and choose the next measurement location based on how much it will learn from it. This approach is more transparent than other methods, which can be mysterious and difficult to understand. ADS can be used for any type of data collection, without needing to train a new model for each task. It has been shown to work well in Magnetic Resonance Imaging, a medical imaging technique that requires collecting many measurements. |
Keywords
* Artificial intelligence * Diffusion * Supervised * Tracking
