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:
Nov. 04, 2025

Filed:

Apr. 12, 2021
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Dilip Krishnan, Arlington, MA (US);

Prannay Khosla, Cambridge, MA (US);

Piotr Teterwak, Boston, MA (US);

Aaron Yehuda Sarna, Cambridge, MA (US);

Aaron Joseph Maschinot, Somerville, MA (US);

Ce Liu, Cambridge, MA (US);

Philip John Isola, Cambridge, MA (US);

Yonglong Tian, Cambridge, MA (US);

Chen Wang, Jersey City, NJ (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/2431 (2023.01); G06N 3/09 (2023.01); G06V 10/44 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/454 (2022.01); G06F 18/214 (2023.01); G06F 18/2178 (2023.01); G06F 18/22 (2023.01); G06F 18/2431 (2023.01); G06N 3/08 (2013.01); G06N 3/09 (2023.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
Abstract

The present disclosure provides an improved training methodology that enables supervised contrastive learning to be simultaneously performed across multiple positive and negative training examples. In particular, example aspects of the present disclosure are directed to an improved, supervised version of the batch contrastive loss, which has been shown to be very effective at learning powerful representations in the self-supervised setting. Thus, the proposed techniques adapt contrastive learning to the fully supervised setting and also enable learning to occur simultaneously across multiple positive examples.


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