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
2.1 Generative algorithms 生成算法可分为两类:显式密度模型和隐式密度模型。 2.1,1 Explicit density model 显式密度模型假设分布,利用真实数据训练包含分布或拟合分布参数的模型。完成后,使用所学习的模型或分布生成新的示例。 2.1.2 Implicit density model 隐式密度模型不能直接估计或拟合数据分布。它在没有明确...
GENERATIVE adversarial networksCONVOLUTIONAL neural networksCOMPUTER visionARTIFICIAL intelligenceIMAGE intensifiersGenerative adversarial network, in short GAN, is a new convolution neural network (CNN) based framework with the great potential to determine high dimensional data from its feedback. It is a ...
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 systematic literature review was conducted following the method outlined by Oates [64], focusing on publications related to “GANs” and their application in “medical image reconstruction.” The search utilized the following keywords: “generative adversarial networks” or “generative adversarial netwo...
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art 2024, Machine Learning: Science and Technology The use of generative adversarial networks in medical image augmentation 2023, Neural Computing and Applications A Survey on Medical Image Segmentation Based on Deep Lear...
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): VAEs 是一种生成模型,它们通过编码输入数据到一个潜在...
GANs: Generative adversarial networks AI: Artificial intelligence WNNs: Wavelet neural networks PSOs: Particle swarm optimization RPROP: Resilient backpropagation GAs: Genetic algorithms AAC: Average article citation ACA: Average citation per article
1、总述论文:AReviewon Generative AdversarialNetworks: Algorithms, Theory,andApplications生成器和判别器两个任务博弈互相训练,互相促进。后续补充内容。 智能推荐 论文阅读:A Critical Review of Recurrent Neural Networks for Sequence Learning 论文阅读:A Critical Review of Recurrent Neural Networks for Sequence ...
2. Review process 3. Theoretical background of generative adversarial networks (GANs) 4. Meta-analysis of GANs in remote sensing 5. Applications of GANs in remote sensing 6. Challenges and future directions 7. Conclusions Declaration of Competing Interest Acknowledgements ReferencesShow full outline Ci...