Related Papers
Generative Adversarial Networks : A Survey
2021 •
Upasna Singh
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...
Review on Generative Adversarial Networks
priyanka shende
Advances of Generative Adversarial Networks: A Survey
2020 •
Christoph Reich
Generative Adversarial Networks (GANs) are part of the deep generative model family and able to generate synthetic samples based on the underlying distribution of real-world data. With expanding interest new discoveries and recent advances are hard to follow. Recent advancements to stabilize training, will help GANs to open up new domains using adjusted architectures and loss functions. Various findings show, that GANS can be used to generate not only images, but is also useful for text and audio creation. This paper, presents an overview of different GAN architectures, giving summaries of the underlying fundamentals of each presented GAN. Furthermore, this paper presents look into four application domains and lists additional domains. Additionally, this paper summaries datasets and metrics used to evaluate GANs and present recent scientific advancements. Keywords–generative adversarial networks; machine learning; deep learning.
INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY
Survey on generative adversarial networks
Ijariit Journal, siva prasad
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.
arXiv (Cornell University)
How Generative Adversarial Networks and Their Variants Work: An Overview of GAN
2017 •
yongjun Hong
International Journal for Research in Applied Science and Engineering Technology (IJRASET)
Generative Adversarial Networks
2021 •
AMEY THAKUR
Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples.
arXiv (Cornell University)
Generative Adversarial Networks and Other Generative Models
2022 •
Markus Wenzel
Generative Adversarial Networks:Introduction and Outlook
IEEE/CAA J. Autom. Sinica
Recently, generative adversarial networks (GANs) have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
International Journal of Multimedia Information Retrieval
Generative adversarial networks: a survey on applications and challenges
2020 •
Prabhu Jayagopal
ArXiv
A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines - From Medical to Remote Sensing
2021 •
Ankan Dash
We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from...
