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1. (WO2018200072) CYCLIC GENERATIVE ADVERSARIAL NETWORK FOR UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION

Pub. No.:    WO/2018/200072    International Application No.:    PCT/US2018/020101
Publication Date: Fri Nov 02 00:59:59 CET 2018 International Filing Date: Thu Mar 01 00:59:59 CET 2018
IPC: G06K 9/62
G06N 3/02
Applicants: NEC LABORATORIES AMERICA, INC.
Inventors: CHOI, Wongun
SCHULTER, Samuel
SOHN, Kihyuk
CHANDRAKER, Manmohan
Title: CYCLIC GENERATIVE ADVERSARIAL NETWORK FOR UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION
Abstract:
A system is provided for unsupervised cross-domain image generation relative to a first and second image domain that each include real images. A first generator generates synthetic images similar to real images in the second domain while including a semantic content of real images in the first domain. A second generator generates synthetic images similar to real images in the first domain while including a semantic content of real images in the second domain. A first discriminator discriminates real images in the first domain against synthetic images generated by the second generator. A second discriminator discriminates real images in the second domain against synthetic images generated by the first generator. The discriminators and generators are deep neural networks and respectively form a generative network and a discriminative network in a cyclic GAN framework configured to increase an error rate of the discriminative network to improve synthetic image quality.