Summary of Rethinking Cluster-conditioned Diffusion Models For Label-free Image Synthesis, by Nikolas Adaloglou and Tim Kaiser and Felix Michels and Markus Kollmann
Rethinking cluster-conditioned diffusion models for label-free image synthesis
by Nikolas Adaloglou, Tim Kaiser, Felix Michels, Markus Kollmann
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A comprehensive experimental study on image-level conditioning for diffusion models is conducted, using cluster assignments to enhance image quality. The impact of individual clustering determinants, such as the number of clusters and clustering method, is investigated across three different datasets. Given the optimal number of clusters for image synthesis, cluster-conditioning achieves state-of-the-art performance with an FID of 1.67 for CIFAR10 and 2.17 for CIFAR100, along with increased training sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image generation models can be improved by using ground truth labels to condition the images. This study looks at how different ways of grouping similar images (called clustering) affect image quality when used in a type of generative model called diffusion models. The researchers tested different methods and numbers of clusters on three different sets of images and found that one method achieved the best results, with an FID score of 1.67 for one set of images and 2.17 for another. This method also allowed for more efficient training. |
Keywords
* Artificial intelligence * Clustering * Diffusion * Generative model * Image generation * Image synthesis
