Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data

Generative adversarial neural networks (GANs) [1] [2] [3] [4] are deep learning (DL) models whereby a dataset is used by an agent, called the generator, to sample white noise from a latent space and simulate a data distribution to create new (fake) data that resemble the original data it has been trained on. Another agent, called the discriminator, has to correctly discern between the original data (provided by the external environment for training) and the fake data (produced by the generator). The generator prevails over the discriminator if the latter does not succeed in distinguishing anymore the original from the fake. The discriminator prevails over the generator if the fake data created by the generator is categorized as fake, and the original data is still categorized as original. An illustration that describes a GANs model is shown in Figure 1.