基于双判别器加权生成对抗网络的图像去模糊方法

原始生成对抗网络(generative adversarial network,GAN)在训练过程中容易产生梯度消失及模式崩溃的问题,去模糊效果不佳.由此本文提出双判别器加权生成对抗网络(dual discriminator weighted generative adversarial network,D2WGAN)...     

Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network

The key to multi-sensor image fusion is the fusion of infrared and visible images. Fusion of infrared and visible images with generative adversarial network(GAN) has great advantages in automatic feature extraction and subjective vision improvement. Due to different principle between infrared and visible imaging, the blur phenomenon of edge and texture is caused in the fusion result of GAN. For this purpose, this paper conducts a novel generative adversarial network with blur suppression. Specif...     

生成对抗网络在医学图像中的应用研究

生成对抗网络(generative adversarial network,GAN)作为深度学习下无监督学习的典型方法,使用深度学习的计算机辅助诊断系统目前已经覆盖病灶检测、病理诊断、放疗规划和术后预测等各临床阶段,在医学图像领域取得了许多显著的成果.首先介绍了医学图像领域存在的基本问题,并简单介绍了生成对抗网络模型的...     

基于GAN指纹库的卷积神经网络室内可见光信道模型

提出一种神经网络算法实现室内可见光信道模型,解决Lambert模型难以计算室内可见光信道的噪声和误差问题。针对指纹库数据量大、难以采集和训练参数多导致迭代速度慢的问题,提出使用生成式对抗网络(generative adversarial network,GAN)生成仿真数据集融合原有的稀疏指纹库,生成满足训练要求数量的指纹库;使用一维的卷积神经网络(convolutional neural network, CNN)提取数据特征,降低训练参数,提高迭代速度。在室内5 m×5 m×3 m环境下采集稀疏指纹库,分别用反向传播 神经网络(back propagation netural network, BPNN)和一维CNN室内可见光信道模型进行对比。仿真结果表明:使用GAN生成指纹库的平均绝对误差为0.04,对数据量增广300%;在同一指纹库下,BPNN信道模型误差为3.81,迭代500次收敛;而CNN信道模型误差为0.79,迭代100次收敛。本文提出的GAN指纹库融合CNN的可见光信道模型具有精度高、误差小、速度快、泛化性强等优点,为室内可见光信道模型提供新的研究方案。     

关于生成式对抗神经网络研究的思路探析

通过对生成式对抗神经网络(Generative Adversarial Networks,简称GAN)进行探析,介绍了深度学习的发展和应用以及GAN的基本思想和应用领域。接着阐述GAN的基础理论,包括生成模型和判别模型、GAN的基本原理和数学模型以及GAN的训练方法和算法。然后介绍GAN的改进算法和技术以及在图像生成、视频生成、文本生成等领域的应用,并讨论了GAN的实用性和可靠性评估。最后探讨GAN的发展趋势和挑战,包括未来的发展方向和趋势、技术挑战和解决方案以及社会和伦理问题。     

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional...     

Dolphin Vocal Sound Generation via Deep WaveGAN

The marine biological sonar system evolved in the struggle of nature is far superior to the current artificial sonar. Therefore, the development of bionic underwater concealed detection is of great strategic significance to the military and economy. In this paper, a generative adversarial network(GAN) is trained based on the dolphin vocal sound dataset we constructed, which can achieve unsupervised generation of dolphin vocal sounds with global consistency. Through the analysis of the generated ...     

Spectrum Prediction Based on GAN and Deep Transfer Learning:A Cross-Band Data Augmentation Framework

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.     

基于GAN数据重构的电信用户流失预测方法

用户是运营商利益的核心。随着携号转网政策的出台,运营商之间的竞争越发激烈。为了提前精准有效地预测用户流失倾向,提出了一种基于生成对抗网络（generative adversarial network,GAN）数据重构的电信用户流失预测方法。首先,利用有效的数据预处理方法电信用户流失数据中的脏数据;其次,利用GAN重构电信用户流失数据,解决电信用户流失数据不平衡问题;最后,利用极度梯度提升树（extremegradient boosting,XGBoost）算法分别训练基于GAN重构的电信用户流失预测模型和基于合成少数类过采样技术（synthetic minority oversampling technique,SMOTE）采样的电信用户流失预测模型,对比两种模型的预测精度。实验结果表明,GAN重构后的电信用户流失预测模型预测精度比未重构的预测模型的准确率提升了6.75%,查准率提升了25.91%,召回率提升了30.91%,F1值提升了28.73%。该方法能够有效提升电信用户流失预测的准确度。     

生成对抗网络人脸生成及其检测技术研究

随着AIGC的突破性进展,内容生成技术成为社会关注的热点。文章重点分析基于GAN的人脸生成技术及其检测方法。首先介绍GAN的原理和基本架构,然后阐述GAN在人脸生成方面的技术模式。重点对基于GAN在人脸语义生成方面的技术框架进行了综述,包括人脸语义生成发展、人脸语义生成的GAN实现。接着从多视图姿态生成、面部年龄改写、人脸的属性风格生成三个方面展开详细的阐述,并从政策法规、检测技术两个方面对伪造生成人脸图片的检测方法进行了分析。文中将检测技术分成基于深度学习、基于物理、基于生理学、基于人类视觉四个方面,最后对检测技术未来方向进行了展望。