Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation and missing data completion. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.
| Version: | 2.1.3 |
| Imports: | Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0), httr (≥ 1.4.7) |
| LinkingTo: | Rcpp |
| Published: | 2024-10-07 |
| DOI: | 10.32614/CRAN.package.ganGenerativeData |
| Author: | Werner Mueller [aut, cre] |
| Maintainer: | Werner Mueller <werner.mueller5 at chello.at> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | TensorFlow (https://www.tensorflow.org) |
| CRAN checks: | ganGenerativeData results |
| Reference manual: | ganGenerativeData.pdf |
| Package source: | ganGenerativeData_2.1.3.tar.gz |
| Windows binaries: | r-devel: ganGenerativeData_2.1.3.zip, r-release: ganGenerativeData_2.1.3.zip, r-oldrel: ganGenerativeData_2.1.3.zip |
| macOS binaries: | r-release (arm64): ganGenerativeData_2.1.3.tgz, r-oldrel (arm64): ganGenerativeData_2.1.3.tgz, r-release (x86_64): ganGenerativeData_2.1.3.tgz, r-oldrel (x86_64): ganGenerativeData_2.1.3.tgz |
| Old sources: | ganGenerativeData archive |
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