对抗网络专题文献集
对抗网络专题文献集
第一篇论文
[生成对抗网](关于它的第一篇文章)
[纸张]:HTTPS://arxiv.org/abs/1406.2661
[代码]:HTTPS://github.com/goodfeli/adversarial
未分类
[使用对角网络的拉普拉斯金字塔的深度生成图像模型]
[纸张] https://arxiv.org/abs/1506.05751
[代码] https://github.com/facebook/eyescream
(具有深卷积生成对抗网络的无监督表示学习)(Gan与卷积网络)(ICLR)
[纸张] https://arxiv.org/abs/1511.06434
[代码] https://github.com/jacobgil/keras-dcgan
[对抗自动编码器]
[纸张] http://arxiv.org/abs/1511.05644
[代码] https://github.com/musyoku/adversarial-autoencoder
[基于深度网络生成具有感知相似性度量的图像]
[纸张] https://arxiv.org/pdf/1602.02644v2.pdf
[生成具有复发性对抗网络的图像]
[纸张] https://arxiv.org/abs/1602.05110
[代码] https://github.com/ofirnachum/sequence_gan
[自然图像歧管的生成视觉操作]
[纸张] https://people.eecs.berkeley.edu/%7Ejunyanz/projects/gvm/eccv16_gvm.pdf
[代码] https://github.com/junyanz/iGAN
[生成对象文本到图像合成]
[纸张] https://arxiv.org/abs/1605.05396
[代码] https://github.com/reedscot/icml2016
[代码] https://github.com/paarthneekhara/text-to-image
[学习什么和在哪里画]
[纸张] http://www.scottreed.info/files/nips2016.pdf
[代码] https://github.com/reedscot/nips2016
[草图检索对抗培训]
[纸张] http://link.springer.com/chapter/10.1007/978-3-319-46604-0_55
[使用风格和结构对抗网络的生成图像建模]
[纸张] https://arxiv.org/pdf/1603.05631.pdf
[代码] https://github.com/xiaolonw/ss-gan
[生成对抗网络作为能量模型的变化训练](ICLR 2017)
[纸张] http://www.mathpubs.com/detail/1611.01799v1/Generative-Adversarial-Networks-as-Variational-Training-of-Energy-Based-Models
[半监督文本分类对抗培训方法](Ian Goodfellow Paper)
[纸张] https://arxiv.org/abs/1605.07725
[注意] https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md
[通过对抗训练从模拟和无监督的图像学习](苹果论文)
[纸张] https://arxiv.org/abs/1612.07828
[代码] https://github.com/carpedm20/simulated-unsupervised-tensorflow
[通过深层发电机网络合成神经网络神经元的首选输入]
[纸张] https://arxiv.org/pdf/1605.09304v5.pdf
[代码] https://github.com/Evolving-AI-Lab/synthesizing
[SalGAN:Visual Saliency Prediction with Generative Adversarial Networks]
[纸张] https://arxiv.org/abs/1701.01081
[代码] https://github.com/imatge-upc/saliency-salgan-2017
[对抗特征学习]
[纸张] https://arxiv.org/abs/1605.09782
[使用循环一致性对抗网络的无图像到图像转换]
[纸张] https://junyanz.github.io/CycleGAN/
[代码] https://github.com/junyanz/CycleGAN
合奏
[AdaGAN:Boosting Generative Models](Google Brain)
[纸张] https://arxiv.org/abs/1701.02386
聚类
[使用生成对抗训练和聚类的无监督学习](ICLR)
[纸张] https://openreview.net/forum?id=SJ8BZTjeg¬eId=SJ8BZTjeg
[代码] https://github.com/VittalP/UnsupGAN
[无监督和半监督学习与分类生成对抗网络](ICLR)
[纸张] https://arxiv.org/abs/1511.06390
图像修复
[感知和语境损失的语义图像修复]
[纸张] https://arxiv.org/abs/1607.07539
[代码] https://github.com/bamos/dcgan-completion.tensorflow
[上下文编码器:通过修复进行功能学习]
[纸张] https://arxiv.org/abs/1604.07379
[代码] https://github.com/jazzsaxmafia/Inpainting
[上下文有条件生成对抗网络的半监督学习]
[纸张] https://arxiv.org/abs/1611.06430v1
联合概率
[对峙学习推论]
[纸张] https://arxiv.org/abs/1606.00704
[代码] https://github.com/IshmaelBelghazi/ALI
超分辨率
[通过深度学习的图像超分辨率](仅面向数据集)
[代码] https://github.com/david-gpu/srez
[使用生成对抗网络的照片逼真单图像超分辨率](使用深度残差网络)
[纸张] https://arxiv.org/abs/1609.04802
[代码] https://github.com/leehomyc/Photo-Realistic-Super-Resoluton
[EnhanceGAN]
[文件] https://medium.com/@richardherbert/faces-from-noise-super-enhancing-8x8-images-with-enhancegan-ebda015bb5e0#.io6pskvin
去除遮蔽
[强大的LSTM自动编码器在野外面部遮挡]
[纸张] https://arxiv.org/abs/1612.08534
语义分割
[使用对话网络的语义分割](soumith的论文)
[纸张] https://arxiv.org/abs/1611.08408
对象检测
[用于小物体检测的感知生成对抗网络](提交)
[A-Fast-RCNN:通过对象检测的对手的硬正产生](CVPR2017)
[纸] http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdfRNN
[C-RNN-GAN:具有对抗性训练的连续循环神经网络]
[纸张] https://arxiv.org/abs/1611.09904
[代码] https://github.com/olofmogren/c-rnn-gan
有条件的对抗
[有条件生成对抗网]
[纸张] https://arxiv.org/abs/1411.1784
[代码] https://github.com/zhangqianhui/Conditional-Gans
[InfoGAN:Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets]
[纸张] https://arxiv.org/abs/1606.03657
[代码] https://github.com/buriburisuri/supervised_infogan
[使用条件对抗网络的图像到图像翻译]
[纸张] https://arxiv.org/pdf/1611.07004v1.pdf
[代码] https://github.com/phillipi/pix2pix
[代码] https://github.com/yenchenlin/pix2pix-tensorflow
[使用辅助分类器GAN的条件图像合成](GoogleBrain ICLR 2017)
[纸张] https://arxiv.org/abs/1610.09585
[代码] https://github.com/buriburisuri/ac-gan
[像素级域名转移]
[纸张] https://arxiv.org/pdf/1603.07442v2.pdf
[代码] https://github.com/fxia22/pldtgan
[图像编辑的可逆条件GAN]
[纸张] https://arxiv.org/abs/1611.06355
[代码] https://github.com/Guim3/IcGAN
[即插即用生成网络:潜在空间中的条件迭代生成图像]
[纸张] https://arxiv.org/abs/1612.00005v1
[代码] https://github.com/Evolving-AI-Lab/ppgn
[StackGAN:文本到具有堆叠生成对话网络的照片逼真图像合成]
[纸张] https://arxiv.org/pdf/1612.03242v1.pdf
[代码] https://github.com/hanzhanggit/StackGAN
[无监督的图像到图像翻译与生成对抗网络]
[纸张] https://arxiv.org/pdf/1701.02676.pdf
[学习与生成对话网络发现跨域关系]
[纸张] https://arxiv.org/abs/1703.05192
[代码] https://github.com/carpedm20/DiscoGAN-pytorch
视频预测
[深度多尺度视频预测超过均方误差](Yann LeCun的论文)
[纸张] https://arxiv.org/abs/1511.05440
[代码] https://github.com/dyelax/Adversarial_Video_Generation
[通过视频预测进行物理互动的无监督学习](Ian Goodfellow的论文)
[纸张] https://arxiv.org/abs/1605.07157
[使用场景动态生成视频]
[纸张] https://arxiv.org/abs/1609.02612
[网络] http://web.mit.edu/vondrick/tinyvideo/
[代码] https://github.com/cvondrick/videogan
纹理合成和风格转移
[使用马尔可夫生成对抗网络的预计算实时纹理合成](ECCV 2016)
[纸张] https://arxiv.org/abs/1604.04382
[代码] https://github.com/chuanli11/MGANs
GAN理论
[能源生成对抗网](Lecun论文)
[纸张] https://arxiv.org/pdf/1609.03126v2.pdf
[代码] https://github.com/buriburisuri/ebgan
[改进GAN培训技巧](Goodfellow的论文)
[纸张] https://arxiv.org/abs/1606.03498
[代码] https://github.com/openai/improved-gan
[模式正则化生成对抗网络](Yoshua Bengio,ICLR 2017)
[纸张] https://openreview.net/pdf?id=HJKkY35le
[改进产生对抗网络的去噪特征匹配](Yoshua Bengio,ICLR 2017)
[纸张] https://openreview.net/pdf?id=S1X7nhsxl
[代码] https://github.com/hvy/chainer-gan-denoising-feature-matching
[采样生成网络]
[纸张] https://arxiv.org/abs/1609.04468
[代码] https://github.com/dribnet/plat
[模式正则化生成对话网络](Yoshua Bengio的论文)
[纸张] https://arxiv.org/abs/1612.02136
[如何训练甘斯]
[的Docu] https://github.com/soumith/ganhacks#authors
[面向训练生成对抗网络的原则方法](ICLR 2017)
[纸张] http://openreview.net/forum?id=Hk4_qw5xe
[展开的生成对抗网络]
[纸张] https://arxiv.org/abs/1611.02163
[代码] https://github.com/poolio/unrolled_gan
[最小二乘法对抗网络]
[纸张] https://arxiv.org/abs/1611.04076
[代码] https://github.com/pfnet-research/chainer-LSGAN
[Wasserstein GAN]
[纸张] https://arxiv.org/abs/1701.07875
[代码] https://github.com/martinarjovsky/WassersteinGAN
[Lipschitz密度损失敏感的生成对抗网络](与WGan相同)
[纸张] https://arxiv.org/abs/1701.06264
[代码] https://github.com/guojunq/lsgan
[面向训练生成对抗网络的原则方法]
[纸张] https://arxiv.org/abs/1701.04862
3D
[通过3D生成 - 对抗建模学习对象形状的概率潜在空间](2016 NIPS)
[纸张] https://arxiv.org/abs/1610.07584
[网络] http://3dgan.csail.mit.edu/
[代码] https://github.com/zck119/3dgan-release
面对生成和编辑
[使用学习的相似性度量自动编码超像素
[纸张] https://arxiv.org/abs/1512.09300
[代码] https://github.com/andersbll/autoencoding_beyond_pixels
[耦合生成对抗网络](NIPS)
[纸张] http://mingyuliu.net/
[Caffe Code] https://github.com/mingyuliutw/CoGAN
[Tensorflow Code] https://github.com/andrewliao11/CoGAN-tensorflow
[图像编辑的可逆条件GAN]
[纸张] https://drive.google.com/file/d/0B48XS5sLi1OlRkRIbkZWUmdoQmM/view
[代码] https://github.com/Guim3/IcGAN
[面部属性操纵的学习残差图像]
[纸张] https://arxiv.org/abs/1612.05363
[使用内省对抗网络进行神经照片编辑](ICLR 2017)
[纸张] https://arxiv.org/abs/1609.07093
[代码] https://github.com/ajbrock/Neural-Photo-Editor
对于离散分布
[最大似然增强离散生成对抗网络]
[纸张] https://arxiv.org/abs/1702.07983v1
[边界寻求生成对抗网络]
[纸张] https://arxiv.org/abs/1702.08431
[GANS-GANSB]的分离元素序列与Gumbel-softmax分布
[纸张] https://arxiv.org/abs/1611.04051
项目
[cleverhans](一个用于对抗脆弱性的对抗图书馆)
[代码] https://github.com/openai/cleverhans
[reset-cppn-gan-tensorflow](使用残差生成对抗网络和变分自动编码器技术来产生高分辨率图像)
[代码] https://github.com/hardmaru/resnet-cppn-gan-tensorflow
(HyperGAN)(开源GAN着重于规模和可用性)
[代码] https://github.com/255bits/HyperGAN