Summary of Ascan: Asymmetric Convolution-attention Networks For Efficient Recognition and Generation, by Anil Kag et al.
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
by Anil Kag, Huseyin Coskun, Jierun Chen, Junli Cao, Willi Menapace, Aliaksandr Siarohin, Sergey Tulyakov, Jian Ren
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 introduces AsCAN, a hybrid neural network architecture that combines convolutional and transformer blocks to achieve promising latency and performance trade-offs. It supports various tasks such as recognition, segmentation, class-conditional image generation, and features a superior trade-off between performance and latency. The proposed asymmetric architecture distribution, with more convolutional blocks in earlier stages and more transformer blocks in later stages, is simple yet effective. AsCAN is scaled to solve large-scale text-to-image tasks and achieves state-of-the-art performance compared to recent public and commercial models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new type of neural network that can do many different things well. It uses special blocks called convolutional and transformer blocks to make decisions quickly and accurately. The blocks are arranged in a way that lets the network learn from lots of data and use it efficiently. This means the network can do tasks like recognizing objects, separating things out, and creating new images. It’s also really fast compared to other networks. |
Keywords
» Artificial intelligence » Image generation » Neural network » Transformer
