Supervised learning techniques for encoder training
US12412089B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 23, 2021 |
| Grant date | Sep 9, 2025 |
| Priority date | — |
| Expiry date | Feb 22, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.