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. 10, 2024

Filed:

Jul. 30, 2021
Applicant:

Samsung Sds America, Inc., San Jose, CA (US);

Inventors:

Heng Hao, San Jose, CA (US);

Sima Didari, San Jose, CA (US);

Jae Oh Woo, Fremont, CA (US);

Hankyu Moon, San Ramon, CA (US);

Patrick David Bangert, Sunnyvale, CA (US);

Assignee:

SAMSUNG SDS AMERICA, INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/2411 (2023.01); G06F 16/28 (2019.01); G06F 16/55 (2019.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 16/283 (2019.01); G06F 16/55 (2019.01); G06F 18/214 (2023.01); G06F 18/2178 (2023.01); G06F 18/2411 (2023.01); G06N 3/08 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30061 (2013.01);
Abstract

A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.


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