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:
Aug. 29, 2023

Filed:

Nov. 29, 2017
Applicant:

Palo Alto Research Center Incorporated, Palo Alto, CA (US);

Inventors:

Sricharan Kallur Palli Kumar, Mountain View, CA (US);

Raja Bala, Pittsford, NY (US);

Jin Sun, Redwood City, MD (US);

Hui Ding, College Park, MD (US);

Matthew A. Shreve, Mountain View, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/08 (2023.01); G06F 18/2413 (2023.01); G06V 10/764 (2022.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/2413 (2023.01); G06N 3/08 (2013.01); G06V 10/451 (2022.01); G06V 10/764 (2022.01);
Abstract

One embodiment facilitates generating synthetic data objects using a semi-supervised GAN. During operation, a generator module synthesizes a data object derived from a noise vector and an attribute label. The system passes, to an unsupervised discriminator module, the data object and a set of training objects which are obtained from a training data set. The unsupervised discriminator module calculates: a value indicating a probability that the data object is real; and a latent feature representation of the data object. The system passes the latent feature representation and the attribute label to a supervised discriminator module. The supervised discriminator module calculates a value indicating a probability that the attribute label given the data object is real. The system performs the aforementioned steps iteratively until the generator module produces data objects with a given attribute label which the unsupervised and supervised discriminator modules can no longer identify as fake.


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