Invention Grant
- Patent Title: Self-supervised sequential variational autoencoder for disentangled data generation
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Application No.: US17088043Application Date: 2020-11-03
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Publication No.: US11423655B2Publication Date: 2022-08-23
- Inventor: Renqiang Min , Yizhe Zhu , Asim Kadav , Hans Peter Graf
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: US NJ Princeton
- Assignee: NEC Laboratories America, Inc.
- Current Assignee: NEC Laboratories America, Inc.
- Current Assignee Address: US NJ Princeton
- Agent Joseph Kolodka
- Main IPC: G06V20/40
- IPC: G06V20/40 ; G06K9/62

Abstract:
A computer-implemented method is provided for disentangled data generation. The method includes accessing, by a variational autoencoder, a plurality of supervision signals. The method further includes accessing, by the variational autoencoder, a plurality of auxiliary tasks that utilize the supervision signals as reward signals to learn a disentangled representation. The method also includes training the variational autoencoder to disentangle a sequential data input into a time-invariant factor and a time-varying factor using a self-supervised training approach which is based on outputs of the auxiliary tasks obtained by using the supervision signals to accomplish the plurality of auxiliary tasks.
Public/Granted literature
- US20210142120A1 SELF-SUPERVISED SEQUENTIAL VARIATIONAL AUTOENCODER FOR DISENTANGLED DATA GENERATION Public/Granted day:2021-05-13
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