RGAN: Generative Adversarial Nets (GAN) in R

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

Version: 0.1.1
Imports: cli, torch, viridis
Published: 2022-03-29
Author: Marcel Neunhoeffer ORCID iD [aut, cre]
Maintainer: Marcel Neunhoeffer <marcel.neunhoeffer at gmail.com>
BugReports: https://github.com/mneunhoe/RGAN/issues
License: MIT + file LICENSE
URL: https://github.com/mneunhoe/RGAN
NeedsCompilation: no
Materials: README NEWS
CRAN checks: RGAN results


Reference manual: RGAN.pdf


Package source: RGAN_0.1.1.tar.gz
Windows binaries: r-devel: RGAN_0.1.1.zip, r-release: RGAN_0.1.1.zip, r-oldrel: RGAN_0.1.1.zip
macOS binaries: r-release (arm64): RGAN_0.1.1.tgz, r-oldrel (arm64): RGAN_0.1.1.tgz, r-release (x86_64): RGAN_0.1.1.tgz, r-oldrel (x86_64): RGAN_0.1.1.tgz


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