Google llc (20240265586). GENERATING HIGH-RESOLUTION IMAGES USING SELF-ATTENTION simplified abstract
Contents
GENERATING HIGH-RESOLUTION IMAGES USING SELF-ATTENTION
Organization Name
Inventor(s)
Han Zhang of Sunnyvale CA (US)
Zizhao Zhang of San Jose CA (US)
GENERATING HIGH-RESOLUTION IMAGES USING SELF-ATTENTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265586 titled 'GENERATING HIGH-RESOLUTION IMAGES USING SELF-ATTENTION
Simplified Explanation: This patent application describes methods, systems, and apparatus for generating high-resolution images using self-attention based neural networks.
Key Features and Innovation:
- Neural network with first and second network blocks for image generation.
- First network blocks apply self-attention mechanism and upsample input.
- Second network blocks process input using neural network layers and upsample output.
Potential Applications: This technology can be used in various fields such as medical imaging, satellite imaging, video processing, and computer vision.
Problems Solved: This technology addresses the challenge of generating high-resolution images efficiently and effectively using neural networks.
Benefits:
- Improved image quality and resolution.
- Faster image generation process.
- Enhanced performance in image processing tasks.
Commercial Applications: Potential commercial applications include image editing software, surveillance systems, medical imaging devices, and satellite imaging technology.
Prior Art: Researchers can explore prior art related to self-attention mechanisms in neural networks and image generation techniques.
Frequently Updated Research: Stay updated on advancements in self-attention mechanisms, neural network architectures for image generation, and applications of high-resolution image processing.
Questions about high-resolution image generation using self-attention based neural networks: 1. How does self-attention mechanism improve image generation in neural networks? 2. What are the key differences between first and second network blocks in this technology?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating high-resolution images using self-attention based neural networks. one of the systems includes a neural network configured to generate images, the neural network comprising a sequence of one or more first network blocks followed by a sequence of one or more second network blocks, wherein: each first network block is configured to perform operations comprising: applying a self-attention mechanism over at least a subset of first elements of a first block input to generate an updated first block input; and upsampling the updated first block input to generate a first block output; and each second network block is configured to perform operations comprising: processing a second block input using one or more neural network layers to generate an updated second block input; and upsampling the updated second block input to generate a second block output.
