Summary of Efficient Generative Adversarial Networks Using Linear Additive-attention Transformers, by Emilio Morales-juarez and Gibran Fuentes-pineda
Efficient generative adversarial networks using linear additive-attention Transformers
by Emilio Morales-Juarez, Gibran Fuentes-Pineda
First submitted to arxiv on: 17 Jan 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 Medium Difficulty summary: This paper presents a novel Generative Adversarial Network (GAN) architecture called LadaGAN, which employs a linear attention Transformer block named Ladaformer to reduce computational complexity and overcome training instabilities. The proposed Ladaformer module replaces traditional dot-product attention with a linear additive-attention mechanism, achieving efficiency gains while maintaining performance. Compared to existing convolutional and Transformer GANs, LadaGAN outperforms on benchmark datasets at different resolutions while requiring significantly fewer computational resources. Additionally, LadaGAN demonstrates competitive performance compared to state-of-the-art multi-step generative models using much less computational power. This work has the potential to enable more widespread adoption of deep generative models in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper introduces a new way to make computer programs that can create images, called LadaGAN. These programs are important for things like art and movies. The problem is that these programs take up a lot of computing power and energy, making them hard to use. The scientists in this paper came up with a new way to build these image-making programs that uses less power and energy. Their new program, called LadaGAN, does just as well as other programs that are more powerful and energy-hungry. This is exciting because it could make it easier for people to use these programs to create cool things. |
Keywords
* Artificial intelligence * Attention * Dot product * Gan * Generative adversarial network * Transformer
