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. 27, 2022

Filed:

Oct. 21, 2019
Applicant:

The Boeing Company, Chicago, IL (US);

Inventors:

Jai Choi, Sammamish, WA (US);

Zachary Jorgensen, Parker, CO (US);

Dragos Margineantu, Bellevue, WA (US);

Tyler Staudinger, Parker, CO (US);

Assignee:

The Boeing Company, Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/30 (2014.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G10L 15/10 (2006.01); G06T 9/00 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 3/0454 (2013.01); G06T 9/002 (2013.01); G10L 15/10 (2013.01); H04N 19/30 (2014.11);
Abstract

A method of machine learning model development includes building an autoencoder including an encoder trained to map an input into a latent representation, and a decoder trained to map the latent representation to a reconstruction of the input. The method includes building an artificial neural network classifier including the encoder, and a classification layer partially trained to perform a classification in which a class to which the input belongs is predicted based on the latent representation. Neural network inversion is applied to the classification layer to find inverted latent representations within a decision boundary between classes in which a result of the classification is ambiguous, and inverted inputs are obtained from the inverted latent representations. Each inverted input is labeled with a class that is its ground truth, and thereby producing added training data for the classification, and the classification layer is further trained using the added training data.


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