Nvidia corporation (20240104345). NEURAL NETWORK ARCHITECTURE SELECTION simplified abstract

NEURAL NETWORK ARCHITECTURE SELECTION

Organization Name

nvidia corporation

Inventor(s)

Cheng Peng of Cockeysville MD (US)

Andriy Myronenko of San Francisco CA (US)

Ali Hatamizsadeh of Los Angeles CA (US)

Vishwesh Nath of Nashville TN (US)

Md Mahfuzur Rahman Siddiquee of Tempe AZ (US)

Yufan He of Philadelphia PA (US)

Daguang Xu of Potomac MD (US)

Dong Yang of Pocatello ID (US)

NEURAL NETWORK ARCHITECTURE SELECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104345 titled 'NEURAL NETWORK ARCHITECTURE SELECTION

Simplified Explanation

The abstract describes a patent application related to the use of neural networks to generate images representing realistic motion or activity.

• One or more neural networks are used to select a first neural network to perform a first task based on performance estimated by a second neural network.

Potential Applications

This technology could be applied in various fields such as:

• Virtual reality and augmented reality
• Video game development
• Animation and film industry

Problems Solved

This technology helps in:

• Creating more realistic and dynamic images
• Improving the quality of motion representation
• Enhancing user experience in virtual environments

Benefits

The benefits of this technology include:

• Increased realism in generated images
• Enhanced user engagement
• Improved efficiency in image generation processes

Potential Commercial Applications

This technology could be commercially applied in:

• Entertainment industry
• Gaming industry
• Simulation and training programs

Possible Prior Art

One possible prior art could be the use of traditional animation techniques to create motion in images. However, the use of neural networks for selecting and performing tasks in image generation is a novel approach.

How does this technology compare to traditional animation techniques in terms of efficiency and realism?

This article does not provide a direct comparison between this technology and traditional animation techniques in terms of efficiency and realism.

What are the limitations of using neural networks for image generation in terms of complexity and computational resources?

This article does not address the potential limitations of using neural networks for image generation in terms of complexity and computational resources.

Original Abstract Submitted

apparatuses, systems, and techniques are presented to generate images representing realistic motion or activity. in at least one embodiment, one or more neural networks are used to select a first neural network to perform a first task based, at least in part, upon a performance estimated by a second neural network.