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Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies
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. This paper reviews
the regularization methods that have been recently introduced, most of which
have been published in the last three years. Specifically, we focus on general
methods that can be commonly used regardless of neural network architectures.
To explore the latest research trends in the regularization for GANs, the
methods are classified into several groups by their operation principles, and
the differences between the methods are analyzed. Furthermore, to provide
practical knowledge of using these methods, we investigate popular methods that
have been frequently employed in state-of-the-art GANs. In addition, we discuss
the limitations in existing methods and propose future research directions