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:
May. 10, 2022

Filed:

Jun. 24, 2020
Applicant:

Fujitsu Limited, Kawasaki, JP;

Inventors:

Wei Shen, Beijing, CN;

Rujie Liu, Beijing, CN;

Assignee:

FUJITSU LIMITED, Kawasaki, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 3/08 (2006.01); G06V 10/40 (2022.01); G06V 30/194 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06K 9/627 (2013.01); G06N 3/08 (2013.01); G06V 10/40 (2022.01); G06V 30/194 (2022.01);
Abstract

An information processing apparatus includes a processor to input each sample image into feature extracting components to obtain at least two features of the sample image, and to cause a classifying component to calculate a classification loss of the sample image based on the at least two features; extract, from each pair of features, a plurality of sample pairs for calculating mutual information between each pair of features; input the plurality of sample pairs into a machine learning architecture corresponding to each pair of features, to calculate an information loss between each pair of features. The processor is to adjust parameters of the feature extracting components, the classifying component and the machine learning architecture by minimizing a sum of classification losses and information losses of sample images in the training set based upon the obtained at least two features of the sample image to calculate the classification losses and the information losses, to obtain the at least two feature extracting components and the classifying component having been trained.


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