Summary of Going Beyond Compositions, Ddpms Can Produce Zero-shot Interpolations, by Justin Deschenaux et al.
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
by Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos, Volkan Cevher
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Denoising Diffusion Probabilistic Models (DDPMs) have shown impressive capabilities in image generation, with studies suggesting they can generalize by composing latent factors learned from the training data. This work takes DDPMs a step further by studying models trained on strictly separate subsets of the data distribution with large gaps on the support of the latent factors. The results show that such models can effectively generate images in unexplored, intermediate regions of the distribution. For instance, when trained on clearly smiling and non-smiling faces, the model demonstrates a sampling procedure that can generate slightly smiling faces without reference images (zero-shot interpolation). This capability is replicated for other attributes and datasets. The code for this study is available at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper studies a type of computer program called Denoising Diffusion Probabilistic Models (DDPMs). These programs are great at creating realistic images. The researchers wanted to see if they could use DDPMs to create new images that are in between two different types of images, like smiling and not smiling faces. They found out that yes, they can do this! The program can even create new images without seeing any examples of the type of image it’s trying to create (called zero-shot interpolation). This is important because it could be used to create new images in many different areas, like art or medicine. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Zero shot
