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:
Aug. 11, 2026

Filed:

May. 06, 2021
Applicant:

Waymo Llc, Mountain View, CA (US);

Inventors:

Ming Ji, Palo Alto, CA (US);

Edward Stephen Walker, Jr., San Francisco, CA (US);

Yang Song, San Jose, CA (US);

Zijian Guo, Sunnyvale, CA (US);

Congcong Li, Cupertino, CA (US);

Assignee:

Waymo LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/088 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06N 3/045 (2023.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor. In one aspect, a method includes obtaining data specifying the teacher machine learning model that is configured to perform a machine learning task; obtaining first training data; training the teacher machine learning model on the first training data to obtain a trained teacher machine learning model; generating second, automatically labeled training data by using the trained teacher machine learning model to process unlabeled training data; and training a student machine learning model to perform the machine learning task using at least the second, automatically labeled training data, wherein the student machine learning model does not include the pre-trained feature extractor and instead includes a different feature extractor having fewer parameters than the pre-trained feature extractor.


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