The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 26, 2022

Filed:

May. 05, 2020
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Jesse Clayton, Santa Clara, CA (US);

Vladimir Glavtchev, Mountain View, CA (US);

Assignee:

Nvidia Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06K 9/00 (2022.01); G06T 15/00 (2011.01); G06K 9/66 (2006.01); G06N 5/04 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06K 9/00221 (2013.01); G06K 9/00335 (2013.01); G06K 9/00369 (2013.01); G06K 9/00805 (2013.01); G06K 9/00825 (2013.01); G06K 9/6255 (2013.01); G06K 9/6292 (2013.01); G06K 9/66 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06T 15/005 (2013.01);
Abstract

The disclosure provides method of training a machine-learning model employing a procedurally synthesized training dataset, a machine that includes a trained machine-learning model, and a method of operating a machine. In one example, the method of training includes: (1) generating training image definitions in accordance with variations in content of training images to be included in a training dataset, (2) rendering the training images corresponding to the training image definitions, (3) generating, at least partially in parallel with the rendering, ground truth data corresponding to the training images, the training images and the ground truth comprising the training dataset, and (4) training a machine-learning model using the training dataset and the ground truth data.


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