Invention Application
- Patent Title: SYSTEMS AND METHODS FOR LEARNING FOR DOMAIN ADAPTATION
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Application No.: US17460691Application Date: 2021-08-30
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Publication No.: US20210389736A1Publication Date: 2021-12-16
- Inventor: Ehsan Hosseini-Asl , Caiming Xiong , Yingbo Zhou , Richard Socher
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Main IPC: G05B13/02
- IPC: G05B13/02 ; G10L21/003 ; G06N3/02

Abstract:
A method for training parameters of a first domain adaptation model. The method includes evaluating a cycle consistency objective using a first task specific model associated with a first domain and a second task specific model associated with a second domain, and evaluating one or more first discriminator models to generate a first discriminator objective using the second task specific model. The one or more first discriminator models include a plurality of discriminators corresponding to a plurality of bands that corresponds domain variable ranges of the first and second domains respectively. The method further includes updating, based on the cycle consistency objective and the first discriminator objective, one or more parameters of the first domain adaptation model for adapting representations from the first domain to the second domain.
Public/Granted literature
- US11676022B2 Systems and methods for learning for domain adaptation Public/Granted day:2023-06-13
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