Refining synthetic data with a generative adversarial network using auxiliary inputs
US10726304B2 · kind B2 · utility
1Cited by
3References
20Claims
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Key dates
| Filing date | Sep 8, 2017 |
| Grant date | Jul 28, 2020 |
| Priority date | — |
| Expiry date | Feb 8, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30256
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention extends to methods, systems, and computer program products for refining synthetic data with a Generative Adversarial Network (GAN) using auxiliary inputs. Refined synthetic data can be rendered more realistically than the original synthetic data. Refined synthetic data also retains annotation metadata and labeling metadata used for training of machine learning models. GANs can be extended to use auxiliary channels as inputs to a refiner network to provide hints about increasing the realism of synthetic data. Refinement of synthetic data enhances the use of synthetic data for additional applications.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.