Denoising diffusion generative adversarial networks
US12333688B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2022 |
| Grant date | Jun 17, 2025 |
| Priority date | — |
| Expiry date | Nov 6, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20182
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Apparatuses, systems, and techniques are presented to train and utilize one or more neural networks. A denoising diffusion generative adversarial network (denoising diffusion GAN) reduces a number of denoising steps during a reverse process. The denoising diffusion GAN does not assume a Gaussian distribution for large steps of the denoising process and applies a multi-model model to permit denoising with fewer steps. Systems and methods further minimize a divergence between a diffused real data distribution and a diffused generator distribution over several timesteps. Accordingly, various embodiments may enable faster sample generation, in which the samples are generated from noise using the denoising diffusion GAN.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.