Scope of generative adversarial networks (GANs) in image processing (original) (raw)

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 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.

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.

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.

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

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2021

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.

Investigation of Different Generative Adversarial Networks Techniques for Image Restoration

International journal of computing and digital system/International Journal of Computing and Digital Systems, 2024

Generative Adversarial Networks are artificial neural networks that pit two different sets of neural networks against one another in order to generate data that isn't part of the training set. The Generative Adversarial Network (GAN) produces good outcomes when they are trained on image data that comes from the actual world. The generator and the discriminator make up the Generative Adversarial Network (GAN), which stands for "generative adversarial network." The parameters that were utilized to generate the data are completely arbitrary. The information is evaluated, and erroneous information is distinguished from true information by the discriminator. GAN has proven to be useful in various domains, such as object recognition, text synthesis, face ageing, image manipulation, image overpainting, image stitching, human pose synthesis, visual salience prediction, stenographic applications, and many more. Several researchers have investigated various types of GANs but comprehensive analysis and comparison of different types of recent GAN's like Deep Convolutional GAN, Wasserstein GAN,Auto Enocoder, Cycle GAN,Progressive GAN and Super Resoultion GAN have not been published so far in the literature. In this article, quantitative and qualitative analysis are carried on to evaluate their suitability of the GAN for a particular application.Examples of applications encompass texture production, facial reconstruction, facial recognition, high-resolution imaging, music composition, drawing creation, cosmetic enhancements, image transformation, voice synthesis, medical diagnostics, and video editing. No single GAN cannot fulfil the desired requirements for all the applications. The article concludes with a discussion of the possible uses of GANs as well as how these applications constitute a fascinating new area of research and prospective expansion.

Generative adversarial networks: a survey on applications and challenges

International Journal of Multimedia Information Retrieval, 2020

Deep neural networks have attained great success in handling high dimensional data, especially images. However, generating naturalistic images containing ginormous subjects for different tasks like image classification, segmentation, object detection, reconstruction, etc., is continued to be a difficult task. Generative modelling has the potential to learn any kind of data distribution in an unsupervised manner. Variational autoencoder (VAE), autoregressive models, and generative adversarial network (GAN) are the popular generative modelling approaches that generate data distributions. Among these, GANs have gained much attention from the research community in recent years in terms of generating quality images and data augmentation. In this context, we collected research articles that employed GANs for solving various tasks from popular databases and summarized them based on their application. The main objective of this article is to present the nuts and bolts of GANs, state-of-the-art related work and its applications, evaluation metrics, challenges involved in training GANs, and benchmark datasets that would benefit naive and enthusiastic researchers who are interested in working on GANs.

Performance Evaluation of Generative Adversarial Networks for Computer Vision Applications

Ingénierie des systèmes d information, 2020

Generative Adversarial Networks (GAN) generates model approaches using Convolution Neural Networks (CNN) to find out learning regularities and to discover the hidden patterns held in given input data. GAN is a generative model that is trained using two models such as generator and Discriminator both competing against each other to learn the probability distribution function, networks such as CNN, RNN, ANN etc. These traditional neural networks are easily fooled in misclassifying things by adding small amount of noise to original data, whereas GAN's are more stable and easier to train due to the amalgamation of Feed Forward Neural Network and CNN. In general, GAN's are simple Neural networks be trained in adversarial way to generate the data mimicking same distribution, Generator learns new possible sample, and the Discriminator learns how to differentiate generated samples from valid facts. Generated samples are similar in the nature but different from real distribution data. The generated samples make use of computer vision techniques such as visualization designs, realistic image generation, image classifications etc. In the proposed work, to realize the probability distribution Restricted-Boltzmann machines and Deep Belief networks are used. The performance of the GAN Networks is evaluated on various standard datasets to realize the complex tasks such as image prediction, handwritten digit's generation, clothing classification, image segmentation tasks etc. From the experimental results, it is clearly evident that the performance of GAN outperforms other state of the art classifiers on all the benchmark datasets.