基于GANs的理论缺陷,在理论分析的基础上,提出了许多基于目标函数的变式来改变GANs的目标函数,如最小二乘生成对抗网络[21]、[22]等。### 3.3.1.1 Least squares generative adversarial networks (LSGANs) :提出了LSGANs[21]、[22]来克服原GANs中的消失梯度问题。结果表明,对于离决策边界较远的样本,原GAN的决策...
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications 1 Introduction GANs由两个模型组成:生成器和鉴别器。生成器试图捕获真实示例的分布,以便生成新的数据样本。鉴别器通常是一个二值分类器,尽可能准确地将生成样本与真实样本区分开来。GANs的优化问题是一个极大极小优化问题。优化终止于...
1.A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications 作者:Jie Gui , Zhenan Sun, Yonggang Wen , Dacheng Tao, Jieping Ye 论文概要:本文试图从算法、理论和应用的角度对各种GAN方法进行综述。首先,详细介绍了大多数GAN算法的动机、数学表示和结构,并比较了它们的共性和不同之处...
A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans. Knowl. Data Eng. 2021, 35, 3313–3332. [Google Scholar] [CrossRef] Hitawala, S. Comparative study on generative adversarial networks. arXiv 2018, arXiv:1801.04271. [Google Scholar] Creswell, A.; ...
These two approaches currently guide the scope of modern cybersecurity studies with generative adversarial networks.doi:10.1007/s10462-019-09717-4Yinka-Banjo, ChikaUniv Lagos AkokaUgot, Ogban-AsuquoArtificial Intelligence Review: An International Science and Engineering Journal...
The breakthrough brought by generative adversarial networks (GANs) in computer vision (CV) applications has gained a lot of attention in different fields d
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples...
This paper reviews Generative Adversarial Networks (GANs) in detail by discussing the strength of the GAN when compared to other generative models, how GAN
关键词:Deeplearning,Generative adversarial network,Generative model,Medical imaging,Review 1. 介绍 随着2012年开始的计算机视觉深度学习的复兴(Krizhevsky等,2012),医学成像中深度学习方法的采用大幅增加。据估计,2016年和2017年在主要医学影像相关会议场所和期刊上发表了400多篇论文(Litjens等,2017)。在医学成像领域...
Generative Adversarial Networks (GANs): GANs 是由生成器和判别器组成的一类神经网络。生成器的目标是创建逼真的数据,而判别器则尝试区分真实数据和生成器产生的假数据。通过这种对抗过程,生成器学习创建越来越逼真的数据。 Variational Autoencoders (VAEs): ...