Summary of Jet: a Modern Transformer-based Normalizing Flow, by Alexander Kolesnikov et al.
Jet: A Modern Transformer-Based Normalizing Flow
by Alexander Kolesnikov, André Susano Pinto, Michael Tschannen
First submitted to arxiv on: 19 Dec 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 The paper revisits the design of coupling-based normalizing flow models for natural images, using Vision Transformer architecture instead of convolutional neural networks. The authors carefully ablate prior design choices and demonstrate state-of-the-art performance with a simpler architecture. Although the visual quality is still behind current state-of-the-art models, strong normalizing flow models can help advance research by serving as building components of more powerful generative models. The paper showcases a promising approach to generating natural images using normalizing flows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a type of generative model called normalizing flows for creating realistic images. It uses a new way of designing these models that makes them better at producing high-quality pictures. Although the results aren’t as good as some other types of models, this approach is important because it can be used to build even more powerful image generators in the future. |
Keywords
» Artificial intelligence » Generative model » Vision transformer
