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)
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