Techniques for domain to domain projection using a generative model
US11880766B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 23, 2021 |
| Grant date | Jan 23, 2024 |
| Priority date | — |
| Expiry date | Dec 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An improved system architecture uses a pipeline including a Generative Adversarial Network (GAN) including a generator neural network and a discriminator neural network to generate an image. An input image in a first domain and information about a target domain are obtained. The domains correspond to image styles. An initial latent space representation of the input image is produced by encoding the input image. An initial output image is generated by processing the initial latent space representation with the generator neural network. Using the discriminator neural network, a score is computed indicating whether the initial output image is in the target domain. A loss is computed based on the computed score. The loss is minimized to compute an updated latent space representation. The updated latent space representation is processed with the generator neural network to generate an output image in the target domain.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.