Invention Grant
- Patent Title: Reciprocating generative models
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Application No.: US16713229Application Date: 2019-12-13
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Publication No.: US11521124B2Publication Date: 2022-12-06
- Inventor: Filipe J. Cabrita Condessa , Jeremy Z. Kolter
- Applicant: Robert Bosch GmbH
- Applicant Address: DE Stuttgart
- Assignee: Robert Bosch GmbH
- Current Assignee: Robert Bosch GmbH
- Current Assignee Address: DE Stuttgart
- Agency: Brooks Kushman P.C.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F17/18 ; G06K9/62

Abstract:
For each generative model of a set of K generative models that classifies sensor data into K classes, in-distribution samples are sampled from training data as being classified as belonging to the class of the generative model and out-of-distribution samples are sampled from the training data as being classified as not belonging to the class of the generative model. Out-of-distribution samples are also generated from each remaining reciprocal generative model in the set of reciprocating generative models excluding the generative model to provide additional samples classified as not belonging to the class of the generative model. Parameters of the generative model are updated to minimize a loss function to maximize likelihood of the samples belonging to the class, and to maximize the loss function on both the sampled out-of-distribution samples and the generated out-of-distribution samples to minimize likelihood of the samples not belonging to the class.
Public/Granted literature
- US20210182731A1 RECIPROCATING GENERATIVE MODELS Public/Granted day:2021-06-17
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