18388178. ATTENTION-BASED IMAGE GENERATION NEURAL NETWORKS simplified abstract (Google LLC)

ATTENTION-BASED IMAGE GENERATION NEURAL NETWORKS

Organization Name

Google LLC

Inventor(s)

Noam M. Shazeer of Palo Alto CA (US)

Lukasz Mieczyslaw Kaiser of San Francisco CA (US)

Jakob D. Uszkoreit of Berlin (DE)

Niki J. Parmar of San Francisco CA (US)

Ashish Teku Vaswani of San Francisco CA (US)

ATTENTION-BASED IMAGE GENERATION NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18388178 titled 'ATTENTION-BASED IMAGE GENERATION NEURAL NETWORKS

Simplified Explanation: The patent application describes methods, systems, and apparatus for generating an output image using a decoder neural network with local masked self-attention sub-layers.

• The method involves generating intensity values for pixel-color channel pairs in a specific order.
• A current output image representation is processed using a decoder neural network to determine intensity values based on a probability distribution.
• Intensity values for pixel-color channel pairs are selected based on the probability distribution.

Key Features and Innovation:

• Generation of output images using a decoder neural network with local masked self-attention sub-layers.
• Determination of intensity values for pixel-color channel pairs based on a probability distribution.
• Selection of intensity values for pixel-color channel pairs in a specific order.

Potential Applications: This technology can be applied in image processing, computer graphics, and artificial intelligence systems.

Problems Solved:

• Efficient generation of output images with accurate intensity values.
• Streamlined process for selecting intensity values for pixel-color channel pairs.

Benefits:

• Improved image generation quality.
• Enhanced performance in image processing tasks.
• Increased efficiency in generating output images.

Commercial Applications: Potential commercial uses include image editing software, graphic design tools, and AI-powered image processing applications.

Prior Art: Research on neural networks for image generation and attention mechanisms in deep learning can provide insights into related technologies.

Frequently Updated Research: Stay updated on advancements in neural network architectures for image generation and attention mechanisms in deep learning.

Questions about Image Generation using Decoder Neural Networks: 1. How does the decoder neural network with local masked self-attention sub-layers improve image generation accuracy? 2. What are the potential limitations of using a decoder neural network for generating output images?

Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output image. In one aspect, one of the methods includes generating the output image intensity value by intensity value according to a generation order of pixel-color channel pairs from the output image, comprising, for each particular generation order position in the generation order: generating a current output image representation of a current output image, processing the current output image representation using a decoder neural network to generate a probability distribution over possible intensity values for the pixel-color channel pair at the particular generation order position, wherein the decoder neural network includes one or more local masked self-attention sub-layers; and selecting an intensity value for the pixel-color channel pair at the particular generation order position using the probability distribution.