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:
Dec. 26, 2023

Filed:

Jan. 19, 2021
Applicant:

Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing, CN;

Inventor:

Fei Tian, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 10/143 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06T 11/001 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/0014 (2013.01); G06V 10/143 (2022.01); G06V 10/764 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 40/171 (2022.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01);
Abstract

A method for training generative network, a method for generating near-infrared image and device. The method includes: obtaining a training sample set, in which the set includes near-infrared image samples and visible-light image samples; obtaining an adversarial network to be trained, in which the generative network of the adversarial network is configured to generate each near-infrared image according to an input visible-light image, the discrimination network of the adversarial network is configured to determine whether an input image is real or generated; constructing a first objective function according to a first distance between each generated near-infrared image and the corresponding near-infrared image sample in an image space and a second distance between each generated near-infrared image and the corresponding near-infrared image sample in a feature space; performing an adversarial training on the adversarial network with the set based on optimizing a value of the first objective function.


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