Generative Adversarial Networks (original) (raw)

Exploring The Potential of Generative Adversarial Network: A Comparative Study Of GAN

IRJET, 2023

Generative Adversarial Networks (GANs), a class of deep learning models that creates new data samples that resemble the original data, are in-depth examined in this research study. The article covers many GAN subtypes, including vanilla GANs, MedGANs, StyleGANs, and CycleGANs, and analyses their designs and training approaches. The study examines the many GAN applications, including text-to-image synthesis, data augmentation, and picture and video creation. There is also discussion of the difficulties each type of GAN method faces, including mode collapse, instability, and vanishing gradients. In-depth analysis is also given to the technical features of GANs, including the generator and discriminator networks, training loss functions, and regularization techniques. The research study examines current advancements in GANs, including self-attention, adversarial autoencoders, and attention mechanisms. Additionally, the paper addresses the ethical issues related to GANs, such as the possible exploitation of data created by GANs and bias in training data. The future potential and developments of GANs are discussed in the study, including its use to unsupervised representation learning and the creation of novel GAN architectures. The study emphasizes the need for more study to overcome GANs' problems and broaden their application to other fields. GANs are a fast-developing subject of study with enormous potential in many areas.

Generative Adversarial Networks and Other Generative Models

arXiv (Cornell University), 2022

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be highly successful-though by no means from the first attempt. This chapter gives a basic introduction into the motivation for Generative Adversarial Networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism, and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, and also typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.

Survey on generative adversarial networks

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

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.

Review on Generative Adversarial Networks

In the last few years, a type of generative model known as Generative Adversarial Networks (GANs), has achieved tremendous success mainly in the field of computer vision, image classification, speech and language processing, etc. GANs are the models which are used to produce new samples which have similar data distribution as of the training dataset. In this review paper, we will first introduce the idea behind the GANs, followed by a brief overview of various types of GANs as well as comparing it with different generative models. Then, we will discuss the application range and finally the future work with its associated research frontiers.

How Generative Adversarial Networks and its variants Work: An Overview of GAN

arXiv (Cornell University), 2017

Generative Adversarial Networks(GAN) gets wide attention in machine learning field because of its massive potential to learn high dimensional, complex real data. Specifically, it does not need to do further distribution assumption and can simply infer real-like samples from latent space. This powerful property leads GAN to be applied various applications such as image synthesis, image attribute editing, image translation, domain adaptation and even to other academic fields. In this review paper, we aim to look into details of GAN. We discuss how GAN operates and fundamental meaning of various objective functions suggested. Then, we focus on how GAN can be combined with auto encoder framework which makes possible latent space to be managed. As an extension, we also point to GAN variants which are applied to various amount of tasks and other fields.

How Generative Adversarial Networks and Their Variants Work: An Overview of GAN

arXiv (Cornell University), 2017

Generative Adversarial Networks (GAN) 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 leads GAN to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation and other academic fields. In this paper, we aim to discuss the details of GAN for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives. In addition, we explain how GAN 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 GAN for their research.

Advances of Generative Adversarial Networks: A Survey

2020

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.

The generative adversarial networks and its application in machine vision

Enterprise Information Systems, 2019

In recent years, the model of improved GAN has been widely applied in the field of machine vision. It not only covers the traditional image processing, but also includes image conversion, image synthesis and so on.. Firstly, this paper describes the basic principles and existing problems of GAN, then introduces several improved GAN models, including Info-GAN, DC-GAN, f-GAN, Cat-GAN and others. Secondly, several improved GAN models for different applications in the field of machine vision are described. Finally, the future trend and development of GAN are prospected.

Recent Advances of Generative Adversarial Networks in Computer Vision

IEEE Access, 2018

The appearance of generative adversarial networks (GAN) provides a new approach and framework for computer vision. Compared with traditional machine learning algorithms, GAN works via adversarial training concept and is more powerful in both feature learning and representation. GAN also exhibits some problems, such as non-convergence, model collapse, and uncontrollability due to high degree of freedom. How to improve the theory of GAN and apply it to computer-vision-related tasks have now attracted much research efforts. In this paper, recently proposed GAN models and their applications in computer vision are systematically reviewed. In particular, we firstly survey the history and development of generative algorithms, the mechanism of GAN, its fundamental network structures, and theoretical analysis of the original GAN. Classical GAN algorithms are then compared comprehensively in terms of the mechanism, visual results of generated samples, and Frechet Inception Distance. These networks are further evaluated from network construction, performance, and applicability aspects by extensive experiments conducted over public datasets. After that, several typical applications of GAN in computer vision, including high-quality samples generation, style transfer, and image translation, are examined. Finally, some existing problems of GAN are summarized and discussed and potential future research topics are forecasted. INDEX TERMS Deep learning, generative adversarial networks (GAN), computer vision (CV), image generation, style transfer, image inpainting.

Generative Adversarial Networks (GAN): A Gentle Introduction [UPDATED]

In this tutorial, I present an intuitive introduction to the Generative Adversarial Network (GAN), invented by Ian Goodfellow of Google Brain, overview the general idea of the model, and describe the algorithm for training it as per the original work. I further briefly introduce the application of GAN in Natural Language Processing to show its flexibility and strong potential as a neural network architecture. In lieu of the discussion, I also present simple Tensorflow code for the original GAN and an important variant --- Wasserstein GAN, to help the reader getting a quick start in practical applications.