Summary of Revisiting Non-autoregressive Transformers For Efficient Image Synthesis, by Zanlin Ni et al.
Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis
by Zanlin Ni, Yulin Wang, Renping Zhou, Jiayi Guo, Jinyi Hu, Zhiyuan Liu, Shiji Song, Yuan Yao, Gao Huang
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the potential of non-autoregressive Transformers (NATs) in image synthesis, revisiting their training and inference strategies to improve performance. While NATs are fast, they lag behind diffusion models in terms of performance. The authors identify complexities in configuring these strategies and propose an automatic framework, AutoNAT, to solve optimal strategies. This results in a notable advancement in NATs’ performance, comparable to the latest diffusion models at reduced inference cost. The paper validates AutoNAT on four benchmark datasets: ImageNet-256 & 512, MS-COCO, and CC3M. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make computers better at generating images quickly. They’re looking at a type of AI called non-autoregressive Transformers (NATs) that can create images fast but aren’t as good as other methods. The scientists found some problems with how NATs work and came up with a new way to make them better, called AutoNAT. This new method is really good at making images quickly, just like the other AI methods. It works well on lots of different image datasets. |
Keywords
» Artificial intelligence » Autoregressive » Image synthesis » Inference
