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:
Jun. 17, 2025

Filed:

Jul. 23, 2021
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Cameron Smith, Santa Cruz, CA (US);

Ratheesh Kalarot, San Jose, CA (US);

Wei-An Lin, San Jose, CA (US);

Richard Zhang, San Francisco, CA (US);

Niloy Mitra, London, GB;

Elya Shechtman, Seattle, WA (US);

Shabnam Ghadar, Menlo Park, CA (US);

Zhixin Shu, San Jose, CA (US);

Yannick Hold-Geoffrey, San Jose, CA (US);

Nathan Carr, San Jose, CA (US);

Jingwan Lu, Santa Clara, CA (US);

Oliver Wang, Seattle, WA (US);

Jun-Yan Zhu, San Jose, CA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 3/04845 (2022.01); G06F 3/04847 (2022.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06T 3/02 (2024.01); G06T 3/18 (2024.01); G06T 3/40 (2024.01); G06T 3/4038 (2024.01); G06T 3/4046 (2024.01); G06T 5/20 (2006.01); G06T 5/77 (2024.01); G06T 11/00 (2006.01); G06T 11/60 (2006.01); G06V 10/28 (2022.01); G06V 10/98 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 3/04845 (2013.01); G06F 3/04847 (2013.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06T 3/02 (2024.01); G06T 3/18 (2024.01); G06T 3/40 (2013.01); G06T 3/4038 (2013.01); G06T 3/4046 (2013.01); G06T 5/20 (2013.01); G06T 5/77 (2024.01); G06T 11/001 (2013.01); G06T 11/60 (2013.01); G06V 10/28 (2022.01); G06V 10/98 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2210/22 (2013.01);
Abstract

An improved system architecture uses a Generative Adversarial Network (GAN) including a specialized generator neural network to generate multiple resolution output images. The system produces a latent space representation of an input image. The system generates a first output image at a first resolution by providing the latent space representation of the input image as input to a generator neural network comprising an input layer, an output layer, and a plurality of intermediate layers and taking the first output image from an intermediate layer, of the plurality of intermediate layers of the generator neural network. The system generates a second output image at a second resolution different from the first resolution by providing the latent space representation of the input image as input to the generator neural network and taking the second output image from the output layer of the generator neural network.


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