kl_real

Discriminator scores on real examples ($D(x)$)

dis_real

Utility function for the kl WGAN loss for real examples as described in <a href="https://arxiv.org/abs/1910.09779">https://arxiv.org/abs/1910.09779</a>
and implemented in python in <a href="https://github.com/ermongroup/f-wgan">https://github.com/ermongroup/f-wgan</a>.

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.

Marcel Neunhoeffer

RGAN

Generative Adversarial Nets (GAN) in R

kl_real function

Utility function for the kl WGAN loss for real examples as described in <a href='https://arxiv.org/abs/1910.09779'>https://arxiv.org/abs/1910.09779</a>
and implemented in python in <a href='https://github.com/ermongroup/f-wgan'>https://github.com/ermongroup/f-wgan</a>.

kl_real: KL WGAN loss on real examples

Description

Usage

Arguments

Value