Invention Grant
- Patent Title: Training a generative model and a discriminative model
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Application No.: US16924731Application Date: 2020-07-09
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Publication No.: US11314989B2Publication Date: 2022-04-26
- Inventor: Andres Mauricio Munoz Delgado
- Applicant: Robert Bosch GmbH
- Applicant Address: DE Stuttgart
- Assignee: Robert Bosch GmbH
- Current Assignee: Robert Bosch GmbH
- Current Assignee Address: DE Stuttgart
- Agency: Norton Rose Fulbright US LLP
- Agent Gerard Messina
- Priority: EP19187339 20190719
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/08

Abstract:
A system for training a generative model and a discriminative model. The generative model generates synthetic instances from latent feature vectors by generating an intermediate representation from the latent feature vector and generating the synthetic instance from the intermediate representation. The discriminative model determines multiple discriminator scores for multiple parts of an input instance, indicating whether the part is from a synthetic instance or an actual instance. The generative model is trained by backpropagation. During the backpropagation, partial derivatives of the loss with respect to entries of the intermediate representation are updated based on a discriminator score for a part of the synthetic instance, wherein the part of the synthetic instance is generated based at least in part on the entry of the intermediate representation, and wherein the partial derivative is decreased in value if the discriminator score indicates an actual instance.
Public/Granted literature
- US20210019572A1 TRAINING A GENERATIVE MODEL AND A DISCRIMINATIVE MODEL Public/Granted day:2021-01-21
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