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:
Nov. 14, 2023

Filed:

Jul. 17, 2019
Applicant:

Nippon Telegraph and Telephone Corporation, Tokyo, JP;

Inventors:

Go Irie, Tokyo, JP;

Yu Mizutsumi, Hiroshima, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/08 (2023.01); G06F 18/2415 (2023.01); G06F 18/10 (2023.01); G06F 18/22 (2023.01); G06V 10/764 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/10 (2023.01); G06F 18/22 (2023.01); G06F 18/2415 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01);
Abstract

An image identification device can be trained to identify classes with high accuracy even in cases with a small number of learning images. Using a first loss function for outputting a value that is smaller the greater a similarity is between the belongingness probability of each class for the image output by the image identification device and a given teacher belongingness probability of the image, and a second loss function for, in a case in which the image input into the image identification device is an actual image, outputting a value that is smaller the smaller the estimated authenticity probability, which expresses how artificial the input image is, output by the image identification device is and for, in a case in which the image input into the image identification device is an artificial image, outputting a value that is smaller the greater the estimated authenticity probability output by the image identification device is, iterative learning of a parameter of the image identification device is executed to reduce the value of the first loss function and the value of the second loss function.


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