Generative Adversarial Networks : A Survey
Upasna Singh2021
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Abstract
Generative Modelling has been a very extensive area of research since it finds immense use cases across multiple domains. Various models have been proposed in the recent past including Fully Visible Belief Nets, NADE, MADE, Pixel RNN Variational Auto Encoders, Markov Chain, and Generative Adversarial Networks. Amongst all the models, Generative Adversarial Networks have been consistently showing huge potential and developments in the area of Art, Music, SemiSupervised learning, Handling Missing data, Drug Discovery, and unsupervised learning. This emerging technology has reshaped the research landscape in the field of generative modeling. The research in the area of Generative Adversarial Networks (GANs) was introduced by Ian J. Goodfellow et al in 2014 [1]. However, since its inception, various models have been proposed over the years and are considered state-of-the-art models in generative modeling. In this survey, we provide a comprehensive review of the original GAN model and it...
Key takeaways
- There are various relevant surveys of GAN that have investigated the various architectures, trends in generative modeling and GANs, and their potential applications in different domains.
- GANs are based on a minimax game [1] in which the Generator directly produces samples, whereas the Discriminator attempts to distinguish between samples drawn from the training data and samples drawn from the Generator [12] The Generator model takes a fixed-length random vector as an input and generates a sample in the domain, such as an image.
- The Bi GAN training objective is defined as under :
- Cycle GAN introduces cycle consistency loss in addition to Adversarial Loss to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y.
- While training another challenge faced by GAN is Mode Collapse.
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GAN stands for Generative Adversarial Networks. GANs are the most interesting topics in Deep Learning. The concept of GAN is introduced by Ian Good Fellow and his colleagues at the University of Montreal. The main architecture of GAN contains two parts: one is a Generator and the other is Discriminator. The name Adversarial stands for conflict and here the conflict is present between Generator and Discriminator. And hence the name adversarial comes to this concept. In this paper, the author has investigated different ways GAN's are used in real time applications and what are the different types of GAN's present. GAN's are mainly important for generating new data from existing ones. As a machine learning model cannot work properly if the size of the dataset is small GAN's are here to help to increase the size by creating new fake things from original ones. GAN's are also used in creating images from the given words that are text-to-image conversion. GANs are also applied in image resolution, image translation and in many other scenarios. From this survey on GAN author aim to know what are the different applications of GAN that are present and their scope. The author has also aimed at knowing the different types of GAN's available at present.
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