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. 29, 2023

Filed:

Aug. 09, 2021
Applicant:

Salesforce.com, Inc., San Francisco, CA (US);

Inventors:

Lily Hu, San Francisco, CA (US);

Caiming Xiong, San Francisco, CA (US);

Richard Socher, San Francisco, CA (US);

Assignee:

salesforce.com, inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/906 (2019.01); G06N 3/088 (2023.01); G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/2413 (2023.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06F 16/55 (2019.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06F 16/55 (2019.01); G06F 16/906 (2019.01); G06F 18/217 (2023.01); G06F 18/24137 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/811 (2022.01); G06V 10/82 (2022.01);
Abstract

Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.


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