Estimating the implicit likelihoods of generative adversarial networks
US11783198B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 3, 2020 |
| Grant date | Oct 10, 2023 |
| Priority date | — |
| Expiry date | Feb 19, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. Presented herein are embodiments to estimate the implicit likelihoods of GAN models. In one or more embodiments, a stable inverse function of the generator is learned with the help of a variance network of the generator. The local variance of the sample distribution may be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on data sets validate embodiments, which outperformed several baseline methods in these tasks. An embodiment was also applied to anomaly detection. Experiments show that the embodiments herein can achieve state-of-the-art anomaly detection performance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.