Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies

Understand

Despite its short history, Generative Adversarial Network (GAN) has been extensively studied and used for various tasks, including its original purpose, i.e., synthetic sample generation.

• However, applying GAN to different data types with diverse neural network architectures has been hindered by its limitation in training, where the model easily diverges.
• Such a notorious training of GANs is well known and has been addressed in numerous studies.
• Consequently, in order to make the training of GAN stable, numerous regularization methods have been proposed in recent years.