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:

Dec. 31, 2019
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Huan Song, San Jose, CA (US);

Shen Yan, East Lansing, MI (US);

Nanxiang Li, San Mateo, CA (US);

Lincan Zou, San Jose, CA (US);

Liu Ren, Cupertino, CA (US);

Assignee:

Robert Bosch GmbH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06N 3/0454 (2013.01);
Abstract

A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data. After domain adaptation, the machine learning system, which is operable in the first domain, is adapted to generate current label data that identifies current sensor data of the second domain.


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