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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.

Patent No.:

US 11748943 B1

PDFFull Text
Date of Patent:
Sep. 05, 2023

Filed:

Feb. 08, 2021

Cleaning dataset for neural network training

Applicant:

Sony Group Corporation, Tokyo, JP;

Inventors:

Jong Hwa Lee, San Diego, CA (US);

Seunghan Kim, San Diego, CA (US);

Gary Lyons, San Diego, CA (US);

Assignee:

SONY GROUP CORPORATION, Tokyo, JP;

Attorney:

CHIP LAW GROUP

Primary Examiner:

Jerome Grant, II

Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06T 17/00 (2006.01); G06T 7/73 (2017.01); G06T 15/04 (2011.01); G06T 7/50 (2017.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 18/214 (2023.01); G06V 10/75 (2022.01); G06V 10/74 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/64 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06T 17/00 (2013.01); G06F 18/214 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/50 (2017.01); G06T 7/73 (2017.01); G06T 15/04 (2013.01); G06V 10/757 (2022.01); G06V 10/761 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/647 (2022.01); G06V 40/171 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01);
Abstract

An electronic device and method of dataset cleaning is provided. The electronic device receives a dataset comprising a plurality of samples, of which a first sample comprises a 2D image of an object of interest and a 3D shape model of the object of interest. The electronic device determines 2D landmarks from the 2D image and extracts 3D landmarks from the 3D shape model. The electronic device computes an error between the determined 2D landmarks and corresponding 2D locations of the extracted 3D landmarks on the 2D image, based on an error metric. Thereafter, the electronic device determines the computed error to be above a threshold. Based on the determination that the computed error is above the threshold, the electronic device updates the dataset by a removal of the first sample from the dataset and trains a neural network on a task of 3D reconstruction, based on the updated dataset.

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