Direct regression encoder architecture and training
US12327188B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 23, 2021 |
| Grant date | Jun 10, 2025 |
| Priority date | — |
| Expiry date | Jan 29, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector lo the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.