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.

Patent No.:

US 11024009 B1

PDF
Full Text
Expired
Date of Patent:
Jun. 01, 2021

Filed:

Sep. 15, 2017
Applicant:

Twitter, Inc., San Francisco, CA (US);

Inventors:

Wenzhe Shi, London, GB;

Christian Ledig, London, GB;

Zehan Wang, London, GB;

Lucas Theis, London, GB;

Ferenc Huszar, Cambridge, GB;

Assignee:

Twitter, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4053 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06T 3/4046 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A neural network is trained to process received visual data to estimate a high-resolution version of the visual data using a training dataset and reference dataset. A set of training data is generated and a generator convolutional neural network parameterized by first weights and biases is trained by comparing characteristics of the training data to characteristics of the reference dataset. The first network is trained to generate super-resolved image data from low-resolution image data and the training includes modifying first weights and biases to optimize processed visual data based on the comparison between the characteristics of the training data and the characteristics of the reference dataset. A discriminator convolutional neural network parameterized by second weights and biases is trained by comparing characteristics of the generated super-resolved image data to characteristics of the reference dataset, and where the second network is trained to discriminate super-resolved image data from real image data.


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