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:
Nov. 10, 2020

Filed:

Mar. 27, 2019
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

Wei Tong, Troy, MI (US);

Chengqi Bian, Troy, MI (US);

Farui Peng, Sterling Heights, MI (US);

Shuqing Zeng, Sterling Heights, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); B60R 11/04 (2006.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06N 20/20 (2019.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 11/001 (2013.01); B60R 11/04 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G06T 5/001 (2013.01); G06T 5/50 (2013.01);
Abstract

A method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.


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