Invention Grant
- Patent Title: Adapting a generative adversarial network to new data sources for image classification
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Application No.: US15850193Application Date: 2017-12-21
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Publication No.: US10540578B2Publication Date: 2020-01-21
- Inventor: Ali Madani , Mehdi Moradi , Tanveer F. Syeda-Mahmood
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Stephen J. Walder, Jr.; William J. Stock
- Main IPC: G06K9/36
- IPC: G06K9/36 ; G06K9/66 ; G06K9/62 ; G16H30/20 ; G06N3/08

Abstract:
Mechanisms are provided to implement a generative adversarial network (GAN) that is trained based on labeled image data, unlabeled image data, and generated image data generated by a generator of the GAN. The GAN comprises a loss function that comprises error components for each of the labeled image data, unlabeled image data, and generated image data which is used to train the GAN. A new data source for which the trained GAN is to be adapted is identified and the trained GAN is adapted for the new data source. Image data in the new data source is classified by applying the adapted GAN to the data in the new data source. Adapting the trained GAN includes obtaining a minimized set of labeled images and utilizing the minimized set of images to perform the adapting of the trained GAN.
Public/Granted literature
- US20190197368A1 Adapting a Generative Adversarial Network to New Data Sources for Image Classification Public/Granted day:2019-06-27
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