Generic image application using GANs (Generative Adversarial Networks): A Review.

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
S P Porkodi, V Sarada, Vivek Maik, K Gurushankar
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引用次数: 0

Abstract

The generative adversarial network (GAN), which has received considerable notice for its outstanding data generating abilities, is one of the most intriguing fields of artificial intelligence study. Large volumes of data are required to develop generalizable deep learning models. GANs are a highly strong class of networks capable of producing believable new pictures from unlabeled source prints and labeled medical imaging data is scarce and costly to produce. Despite GAN's remarkable outcomes, steady training remains a challenge. The goal of this study is to perform a complete evaluation of the GAN-related literature and to present a succinct summary of existing knowledge on GAN, including the theory following it, its intended purpose, potential base model alterations, and latest breakthroughs in the area. This article will aid you in gaining a comprehensive grasp of GAN and provide an overview of GAN and its many model types, as well as common implementations, measurement parameter suggestions, and GAN applications in image processing. It will also go over the several applications of GANs in image processing, as well as their benefits and limitations, as well as its prospective reach.

基于生成对抗网络的通用图像应用综述。
生成对抗网络(GAN)因其出色的数据生成能力而受到广泛关注,是人工智能研究中最有趣的领域之一。开发可推广的深度学习模型需要大量的数据。gan是一种非常强大的网络,能够从未标记的源图像中生成可信的新图像,而标记的医学成像数据是稀缺且昂贵的。尽管GAN取得了显著的成果,但稳定的训练仍然是一个挑战。本研究的目的是对氮化镓相关文献进行全面评估,并对氮化镓的现有知识进行简要总结,包括其理论,其预期目的,潜在的基础模型变更以及该领域的最新突破。本文将帮助您全面掌握GAN,并概述GAN及其许多模型类型,以及常见实现、测量参数建议和GAN在图像处理中的应用。它还将讨论gan在图像处理中的几个应用,以及它们的优点和局限性,以及它的预期范围。
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来源期刊
Evolving Systems
Evolving Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.80
自引率
6.20%
发文量
67
期刊介绍: Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems. ‘Evolving systems’ are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling and related disciplines where the model structure is assumed and fixed a priori and the problem is focused on parametric optimisation, evolving systems allow the model structure to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organization. It can adapt to new data patterns, is more suitable for streaming data, transfer learning and can recognise and learn from unknown and unpredictable data patterns. Such properties are critically important for autonomous, robotic systems that continue to learn and adapt after they are being designed (at run time). Evolving Systems solicits publications that address the problems of all aspects of system modelling, clustering, classification, prediction and control in non-stationary, unpredictable environments and describe new methods and approaches for their design. The journal is devoted to the topic of self-developing, self-organised, and evolving systems in its entirety — from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as Evolving Systems methodology Evolving Neural Networks and Neuro-fuzzy Systems Evolving Classifiers and Clustering Evolving Controllers and Predictive models Evolving Explainable AI systems Evolving Systems applications but also looking at new paradigms and applications, including medicine, robotics, business, industrial automation, control systems, transportation, communications, environmental monitoring, biomedical systems, security, and electronic services, finance and economics. The common features for all submitted methods and systems are the evolving nature of the systems and the environments. The journal is encompassing contributions related to: 1) Methods of machine learning, AI, computational intelligence and mathematical modelling 2) Inspiration from Nature and Biology, including Neuroscience, Bioinformatics and Molecular biology, Quantum physics 3) Applications in engineering, business, social sciences.
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