-
NIP 2016 对抗训练 Workshop
【网页】https://sites.google.com/site/nips2016adversarial/
【博客】http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/
教程 & 博客
-
【博客】https://github.com/soumith/ganhacks
-
NIPS 2016 教程:生成对抗网络
【arXiv】https://arxiv.org/abs/1701.00160
-
深度学习和 GAN 背后的直觉知识——一个基础理解
【博客】https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935
-
OpenAI——生成模型
【博客】https://openai.com/blog/generative-models/
-
SimGANs——无监督学习的游戏规则颠覆者,无人车等
【博客】https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b
论文
理论 & 机器学习
-
生成对抗网络,逆向强化学习和 Energy-Based 模型之间的联系(A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models )
-
可扩展对抗分类的通用训练框架(A General Retraining Framework for Scalable Adversarial Classification)
-
对抗自编码器(Adversarial Autoencoders)
-
对抗判别的领域适应(Adversarial Discriminative Domain Adaptation)
-
对抗性 Generator-Encoder 网络(Adversarial Generator-Encoder Networks)
-
对抗特征学习(Adversarial Feature Learning)
【代码】https://github.com/wiseodd/generative-models
-
对抗推理学习(Adversarially Learned Inference)
【代码】https://github.com/wiseodd/generative-models
-
结构化输出神经网络半监督训练的一种对抗正则化(An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks)
-
联想式对抗网络(Associative Adversarial Networks)
-
b-GAN:生成对抗网络的一个新框架(b-GAN: New Framework of Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
边界寻找生成对抗网络(Boundary-Seeking Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
条件生成对抗网络(Conditional Generative Adversarial Nets)
【代码】https://github.com/wiseodd/generative-models
-
结合生成对抗网络和 Actor-Critic 方法(Connecting Generative Adversarial Networks and Actor-Critic Methods)
-
描述符和生成网络的协同训练(Cooperative Training of Deor and Generator Networks)
-
Coupled Generative Adversarial Networks(CoGAN)
【代码】https://github.com/wiseodd/generative-models
-
基于能量模型的生成对抗网络(Energy-based Generative Adversarial Network)
【代码】https://github.com/wiseodd/generative-models
-
对抗样本的解释和利用(Explaining and Harnessing Adversarial Examples)
-
f-GAN:使用变分发散最小化训练生成式神经采样器(f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization)
-
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
-
用递归对抗网络乘车图像(Generating images with recurrent adversarial networks)
-
Generative Adversarial Nets with Labeled Data by Activation Maximization
-
生成对抗网络(Generative Adversarial Networks)
【代码】https://github.com/goodfeli/adversarial
【代码】https://github.com/wiseodd/generative-models
-
生成对抗并行化(Generative Adversarial Parallelization)
【代码】https://github.com/wiseodd/generative-models
-
One Shot学习的生成性对抗残差成对网络(Generative Adversarial Residual Pairwise Networks for One Shot Learning)
-
生成对抗结构化网络(Generative Adversarial Structured Networks)
-
生成式矩匹配网络(Generative Moment Matching Networks)
【代码】https://github.com/yujiali/gmmn
-
训练GAN的改进技术(Improved Techniques for Training GANs)
【代码】https://github.com/openai/improved-gan
-
改善训练WGAN(Improved Training of Wasserstein GANs)
【代码】https://github.com/wiseodd/generative-models
-
InfoGAN:通过信息最大化GAN学习可解释表示(InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets)
【代码】https://github.com/wiseodd/generative-models
-
翻转GAN的生成器(Inverting The Generator Of A Generative Adversarial Network)
-
隐式生成模型里的学习(Learning in Implicit Generative Models)
-
用GAN学习发现跨域关系(Learning to Discover Cross-Domain Relations with Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
最小二乘生成对抗网络,LSGAN(Least Squares Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
LS-GAN,损失敏感GAN(Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities)
-
LR-GAN:用于图像生成的分层递归GAN(LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation)
-
MAGAN: Margin Adaptation for Generative Adversarial Networks
【代码】https://github.com/wiseodd/generative-models
-
最大似然增强的离散生成对抗网络(Maximum-Likelihood Augmented Discrete Generative Adversarial Networks)
-
模式正则化GAN(Mode Regularized Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
Multi-Agent Diverse Generative Adversarial Networks
-
生成对抗网络中Batch Normalization和Weight Normalization的影响(On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks)
-
基于解码器的生成模型的定量分析(On the Quantitative Analysis of Decoder-Based Generative Models)
-
SeqGAN:策略渐变的序列生成对抗网络(SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient)
-
深度网络的简单黑箱对抗干扰(Simple Black-Box Adversarial Perturbations for Deep Networks)
-
Stacked GAN(Stacked Generative Adversarial Networks)
-
通过最大均值差异优化训练生成神经网络(Training generative neural networks via Maximum Mean Discrepancy optimization)
-
Triple Generative Adversarial Nets
-
Unrolled Generative Adversarial Networks
-
DCGAN无监督表示学习(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)
【代码】https://github.com/Newmu/dcgan_code
【代码】https://github.com/pytorch/examples/tree/master/dcgan
【代码】https://github.com/carpedm20/DCGAN-tensorflow
【代码】https://github.com/jacobgil/keras-dcgan
-
Wasserstein GAN(WGAN)
【代码】https://github.com/martinarjovsky/WassersteinGAN
【代码】https://github.com/wiseodd/generative-models
视觉应用
-
用对抗网络检测恶性前列腺癌(Adversarial Networks for the Detection of Aggressive Prostate Cancer)
-
条件对抗自编码器的年龄递进/回归(Age Progression / Regression by Conditional Adversarial Autoencoder)
-
ArtGAN:条件分类GAN的艺术作品合成(ArtGAN: Artwork Synthesis with Conditional Categorial GANs)
-
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
-
卷积人脸生成的条件GAN(Conditional generative adversarial nets for convolutional face generation)
-
辅助分类器GAN的条件图像合成(Conditional Image Synthesis with Auxiliary Classifier GANs)
【代码】https://github.com/wiseodd/generative-models
-
使用对抗网络的Laplacian金字塔的深度生成图像模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)
【代码】https://github.com/facebook/eyescream
【博客】http://soumith.ch/eyescream/
-
Deep multi-scale video prediction beyond mean square error
【代码】https://github.com/dyelax/Adversarial_Video_Generation
-
DualGAN:图像到图像翻译的无监督Dual学习(DualGAN: Unsupervised Dual Learning for Image-to-Image Translation)
【代码】https://github.com/wiseodd/generative-models
-
用循环神经网络做全分辨率图像压缩(Full Resolution Image Compression with Recurrent Neural Networks)
-
生成以适应:使用GAN对齐域(Generate To Adapt: Aligning Domains using Generative Adversarial Networks)
-
生成对抗文本到图像的合成(Generative Adversarial Text to Image Synthesis)
【代码】https://github.com/paarthneekhara/text-to-image
-
自然图像流形上的生成视觉操作(Generative Visual Manipulation on the Natural Image Manifold)
【项目】http://www.eecs.berkeley.edu/~junyanz/projects/gvm/
【视频】https://youtu.be/9c4z6YsBGQ0
【代码】https://github.com/junyanz/iGAN
-
Image De-raining Using a Conditional Generative Adversarial Network
-
Image Generation and Editing with Variational Info Generative Adversarial Networks
-
用条件对抗网络做 Image-to-Image 翻译(Image-to-Image Translation with Conditional Adversarial Networks)
【代码】https://github.com/phillipi/pix2pix
-
用GAN模仿驾驶员行为(Imitating Driver Behavior with Generative Adversarial Networks)
-
可逆的条件GAN用于图像编辑(Invertible Conditional GANs for image editing)
-
学习驱动模拟器(Learning a Driving Simulator)
-
多视角GAN(Multi-view Generative Adversarial Networks)
-
利用内省对抗网络编辑图片(Neural Photo Editing with Introspective Adversarial Networks)
-
使用GAN生成照片级真实感的单一图像超分辨率(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network)
-
Recurrent Topic-Transition GAN for Visual Paragraph Generation
-
RenderGAN:生成现实的标签数据(RenderGAN: Generating Realistic Labeled Data)
-
SeGAN: Segmenting and Generating the Invisible
-
使用对抗网络做语义分割(Semantic Segmentation using Adversarial Networks)
-
半隐性GAN:学习从特征生成和修改人脸图像(Semi-Latent GAN: Learning to generate and modify facial images from attributes)
-
TAC-GAN - 文本条件辅助分类器GAN(TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network)
-
通过条件GAN实现多样化且自然的图像描述(Towards Diverse and Natural Image Deions via a Conditional GAN)
-
GAN 提高人的体外识别基线的未标记样本生成(Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro)
-
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
-
无监督异常检测,用GAN指导标记发现(Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery)
-
无监督跨领域图像生成(Unsupervised Cross-Domain Image Generation)
-
WaterGAN:实现单目水下图像实时颜色校正的无监督生成网络(WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images)
其他应用
-
基于生成模型的文本分类的半监督学习方法(Adversarial Training Methods for Semi-Supervised Text Classification)
-
学习在面对对抗性神经网络解密下维护沟通保密性(Learning to Protect Communications with Adversarial Neural Cryptography)
【博客】http://t.cn/RJitWNw
-
MidiNet:利用 1D 和 2D条件实现符号域音乐生成的卷积生成网络(MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions)
-
使用生成对抗网络重建三维多孔介质(Reconstruction of three-dimensional porous media using generative adversarial neural networks)
【代码】https://github.com/LukasMosser/PorousMediaGan
-
Semi-supervised Learning of Compact Document Representations with Deep Networks
-
Steganographic GAN(Steganographic Generative Adversarial Networks)
Humor
-
停止 GAN 暴力:生成性非对抗网络(Stopping GAN Violence: Generative Unadversarial Networks)
视频
-
Ian Goodfellow:生成对抗网络
【视频】http://t.cn/RxxJF5A
-
Mark Chang:生成对抗网络教程
【视频】http://t.cn/RXJOKK1
代码
-
Cleverhans:一个对抗样本的机器学习库
【代码】https://github.com/openai/cleverhans
【博客】http://cleverhans.io/
-
50行代码实现GAN(PyTorch)
【代码】https://github.com/devnag/pytorch-generative-adversarial-networks
【博客】https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
-
生成模型集,e.g. GAN, VAE,用 Pytorch 和 TensorFlow 实现
【代码】https://github.com/wiseodd/generative-models
【进入新智元微信公众号,在对话框输入“170501”下载全部 89 篇论文】
原文地址:https://github.com/nightrome/really-awesome-gan
-
NIP 2016 对抗训练 Workshop
【网页】https://sites.google.com/site/nips2016adversarial/
【博客】http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/
教程 & 博客
-
【博客】https://github.com/soumith/ganhacks
-
NIPS 2016 教程:生成对抗网络
【arXiv】https://arxiv.org/abs/1701.00160
-
深度学习和 GAN 背后的直觉知识——一个基础理解
【博客】https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935
-
OpenAI——生成模型
【博客】https://openai.com/blog/generative-models/
-
SimGANs——无监督学习的游戏规则颠覆者,无人车等
【博客】https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b
论文
理论 & 机器学习
-
生成对抗网络,逆向强化学习和 Energy-Based 模型之间的联系(A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models )
-
可扩展对抗分类的通用训练框架(A General Retraining Framework for Scalable Adversarial Classification)
-
对抗自编码器(Adversarial Autoencoders)
-
对抗判别的领域适应(Adversarial Discriminative Domain Adaptation)
-
对抗性 Generator-Encoder 网络(Adversarial Generator-Encoder Networks)
-
对抗特征学习(Adversarial Feature Learning)
【代码】https://github.com/wiseodd/generative-models
-
对抗推理学习(Adversarially Learned Inference)
【代码】https://github.com/wiseodd/generative-models
-
结构化输出神经网络半监督训练的一种对抗正则化(An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks)
-
联想式对抗网络(Associative Adversarial Networks)
-
b-GAN:生成对抗网络的一个新框架(b-GAN: New Framework of Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
边界寻找生成对抗网络(Boundary-Seeking Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
条件生成对抗网络(Conditional Generative Adversarial Nets)
【代码】https://github.com/wiseodd/generative-models
-
结合生成对抗网络和 Actor-Critic 方法(Connecting Generative Adversarial Networks and Actor-Critic Methods)
-
描述符和生成网络的协同训练(Cooperative Training of Deor and Generator Networks)
-
Coupled Generative Adversarial Networks(CoGAN)
【代码】https://github.com/wiseodd/generative-models
-
基于能量模型的生成对抗网络(Energy-based Generative Adversarial Network)
【代码】https://github.com/wiseodd/generative-models
-
对抗样本的解释和利用(Explaining and Harnessing Adversarial Examples)
-
f-GAN:使用变分发散最小化训练生成式神经采样器(f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization)
-
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
-
用递归对抗网络乘车图像(Generating images with recurrent adversarial networks)
-
Generative Adversarial Nets with Labeled Data by Activation Maximization
-
生成对抗网络(Generative Adversarial Networks)
【代码】https://github.com/goodfeli/adversarial
【代码】https://github.com/wiseodd/generative-models
-
生成对抗并行化(Generative Adversarial Parallelization)
【代码】https://github.com/wiseodd/generative-models
-
One Shot学习的生成性对抗残差成对网络(Generative Adversarial Residual Pairwise Networks for One Shot Learning)
-
生成对抗结构化网络(Generative Adversarial Structured Networks)
-
生成式矩匹配网络(Generative Moment Matching Networks)
【代码】https://github.com/yujiali/gmmn
-
训练GAN的改进技术(Improved Techniques for Training GANs)
【代码】https://github.com/openai/improved-gan
-
改善训练WGAN(Improved Training of Wasserstein GANs)
【代码】https://github.com/wiseodd/generative-models
-
InfoGAN:通过信息最大化GAN学习可解释表示(InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets)
【代码】https://github.com/wiseodd/generative-models
-
翻转GAN的生成器(Inverting The Generator Of A Generative Adversarial Network)
-
隐式生成模型里的学习(Learning in Implicit Generative Models)
-
用GAN学习发现跨域关系(Learning to Discover Cross-Domain Relations with Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
最小二乘生成对抗网络,LSGAN(Least Squares Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
LS-GAN,损失敏感GAN(Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities)
-
LR-GAN:用于图像生成的分层递归GAN(LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation)
-
MAGAN: Margin Adaptation for Generative Adversarial Networks
【代码】https://github.com/wiseodd/generative-models
-
最大似然增强的离散生成对抗网络(Maximum-Likelihood Augmented Discrete Generative Adversarial Networks)
-
模式正则化GAN(Mode Regularized Generative Adversarial Networks)
【代码】https://github.com/wiseodd/generative-models
-
Multi-Agent Diverse Generative Adversarial Networks
-
生成对抗网络中Batch Normalization和Weight Normalization的影响(On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks)
-
基于解码器的生成模型的定量分析(On the Quantitative Analysis of Decoder-Based Generative Models)
-
SeqGAN:策略渐变的序列生成对抗网络(SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient)
-
深度网络的简单黑箱对抗干扰(Simple Black-Box Adversarial Perturbations for Deep Networks)
-
Stacked GAN(Stacked Generative Adversarial Networks)
-
通过最大均值差异优化训练生成神经网络(Training generative neural networks via Maximum Mean Discrepancy optimization)
-
Triple Generative Adversarial Nets
-
Unrolled Generative Adversarial Networks
-
DCGAN无监督表示学习(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)
【代码】https://github.com/Newmu/dcgan_code
【代码】https://github.com/pytorch/examples/tree/master/dcgan
【代码】https://github.com/carpedm20/DCGAN-tensorflow
【代码】https://github.com/jacobgil/keras-dcgan
-
Wasserstein GAN(WGAN)
【代码】https://github.com/martinarjovsky/WassersteinGAN
【代码】https://github.com/wiseodd/generative-models
视觉应用
-
用对抗网络检测恶性前列腺癌(Adversarial Networks for the Detection of Aggressive Prostate Cancer)
-
条件对抗自编码器的年龄递进/回归(Age Progression / Regression by Conditional Adversarial Autoencoder)
-
ArtGAN:条件分类GAN的艺术作品合成(ArtGAN: Artwork Synthesis with Conditional Categorial GANs)
-
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
-
卷积人脸生成的条件GAN(Conditional generative adversarial nets for convolutional face generation)
-
辅助分类器GAN的条件图像合成(Conditional Image Synthesis with Auxiliary Classifier GANs)
【代码】https://github.com/wiseodd/generative-models
-
使用对抗网络的Laplacian金字塔的深度生成图像模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)
【代码】https://github.com/facebook/eyescream
【博客】http://soumith.ch/eyescream/
-
Deep multi-scale video prediction beyond mean square error
【代码】https://github.com/dyelax/Adversarial_Video_Generation
-
DualGAN:图像到图像翻译的无监督Dual学习(DualGAN: Unsupervised Dual Learning for Image-to-Image Translation)
【代码】https://github.com/wiseodd/generative-models
-
用循环神经网络做全分辨率图像压缩(Full Resolution Image Compression with Recurrent Neural Networks)
-
生成以适应:使用GAN对齐域(Generate To Adapt: Aligning Domains using Generative Adversarial Networks)
-
生成对抗文本到图像的合成(Generative Adversarial Text to Image Synthesis)
【代码】https://github.com/paarthneekhara/text-to-image
-
自然图像流形上的生成视觉操作(Generative Visual Manipulation on the Natural Image Manifold)
【项目】http://www.eecs.berkeley.edu/~junyanz/projects/gvm/
【视频】https://youtu.be/9c4z6YsBGQ0
【代码】https://github.com/junyanz/iGAN
-
Image De-raining Using a Conditional Generative Adversarial Network
-
Image Generation and Editing with Variational Info Generative Adversarial Networks
-
用条件对抗网络做 Image-to-Image 翻译(Image-to-Image Translation with Conditional Adversarial Networks)
【代码】https://github.com/phillipi/pix2pix
-
用GAN模仿驾驶员行为(Imitating Driver Behavior with Generative Adversarial Networks)
-
可逆的条件GAN用于图像编辑(Invertible Conditional GANs for image editing)
-
学习驱动模拟器(Learning a Driving Simulator)
-
多视角GAN(Multi-view Generative Adversarial Networks)
-
利用内省对抗网络编辑图片(Neural Photo Editing with Introspective Adversarial Networks)
-
使用GAN生成照片级真实感的单一图像超分辨率(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network)
-
Recurrent Topic-Transition GAN for Visual Paragraph Generation
-
RenderGAN:生成现实的标签数据(RenderGAN: Generating Realistic Labeled Data)
-
SeGAN: Segmenting and Generating the Invisible
-
使用对抗网络做语义分割(Semantic Segmentation using Adversarial Networks)
-
半隐性GAN:学习从特征生成和修改人脸图像(Semi-Latent GAN: Learning to generate and modify facial images from attributes)
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TAC-GAN - 文本条件辅助分类器GAN(TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network)
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通过条件GAN实现多样化且自然的图像描述(Towards Diverse and Natural Image Deions via a Conditional GAN)
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GAN 提高人的体外识别基线的未标记样本生成(Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro)
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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无监督异常检测,用GAN指导标记发现(Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery)
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无监督跨领域图像生成(Unsupervised Cross-Domain Image Generation)
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WaterGAN:实现单目水下图像实时颜色校正的无监督生成网络(WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images)
其他应用
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基于生成模型的文本分类的半监督学习方法(Adversarial Training Methods for Semi-Supervised Text Classification)
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学习在面对对抗性神经网络解密下维护沟通保密性(Learning to Protect Communications with Adversarial Neural Cryptography)
【博客】http://t.cn/RJitWNw
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MidiNet:利用 1D 和 2D条件实现符号域音乐生成的卷积生成网络(MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions)
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使用生成对抗网络重建三维多孔介质(Reconstruction of three-dimensional porous media using generative adversarial neural networks)
【代码】https://github.com/LukasMosser/PorousMediaGan
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Semi-supervised Learning of Compact Document Representations with Deep Networks
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Steganographic GAN(Steganographic Generative Adversarial Networks)
Humor
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停止 GAN 暴力:生成性非对抗网络(Stopping GAN Violence: Generative Unadversarial Networks)
视频
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Ian Goodfellow:生成对抗网络
【视频】http://t.cn/RxxJF5A
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Mark Chang:生成对抗网络教程
【视频】http://t.cn/RXJOKK1
代码
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Cleverhans:一个对抗样本的机器学习库
【代码】https://github.com/openai/cleverhans
【博客】http://cleverhans.io/
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50行代码实现GAN(PyTorch)
【代码】https://github.com/devnag/pytorch-generative-adversarial-networks
【博客】https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
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生成模型集,e.g. GAN, VAE,用 Pytorch 和 TensorFlow 实现
【代码】https://github.com/wiseodd/generative-models
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原文地址:https://github.com/nightrome/really-awesome-gan