Method for learning cross-domain relations based on generative adversarial networks
US10713294B2 · kind B2 · utility
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20Claims
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Key dates
| Filing date | Mar 7, 2019 |
| Grant date | Jul 14, 2020 |
| Priority date | — |
| Expiry date | Mar 7, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A generative adversarial networks-based or GAN-based method for learning cross-domain relations is disclosed. A provided architecture includes two coupled GANs: a first GAN learning a translation of images from domain A to domain B, and a second GAN learning a translation of images from domain B to domain A. A loop formed by the first GAN and the second GAN causes sample images to be reconstructed into an original domain after being translated into a target domain. Therefore, loss functions representing reconstruction losses of the images may be used to train generative models.
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