How Generative Adversarial Networks and Their Variants Work

By Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon. 2017

184 citations recorded by the source corpus.

Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

The relativistic discriminator: a key element missing from standard GAN (2018, background): “Generative adversarial networks (GANs) [Hong et al., 2017] form a broad class of generative models in which a game is played between two competing neural networks, the discriminator D and the generator G .”
Monocular depth estimation based on deep learning: An overview (2020, background): “4], the adversarial learning proposed in [62] has become a hot research direction in recent years. Varieties of algorithms, theories, and applications have been widely developed, which is reviewed in [75]. The frameworks of adversarial learning in depth estimation are shown in Fig. 2. Different kind”
Generative Adversarial Networks (GANs) (2020, background): “es, we analyze and discuss them in a systematic way. We also outline major open issues that can be pursued by researchers further. There are a limited number of existing reviews on the topic of GANs. [13] discussed how GANs and stateof-the-art GANs works. [14]–[16] provided a brief introduction of s”
A Survey of Unsupervised Deep Domain Adaptation (2018, background): “For an indepth discussion of these techniques, there are a number of survey papers directed at GAN variants that include a discussion of training challenges and work [93, 100, 158].”