理论

nn https://www.nature.com/articles/nature14539 这篇论文综述了10年神经网络的几个重要节点，可以先了解下(由3巨头编写)

1.cnn 几种网络结构 简单了解即可 Lenet,googlenet,resnet,capsule-net

2.重点 gan papers

✅ [Generative Adversarial Nets] [Paper] [Code](the First paper of GAN)

3.Tutorial

✅ http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]

Super-Resolution

✅ [Image super-resolution through deep learning ][Code](Just for face dataset)

2.重点关注下这篇，超分辨率

✅ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [Paper][Code]（Using Deep residual network）

✅ [EnhanceGAN] [Docs][[Code]]

gen high-quality image

1. 图片聚类

✅ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)(ICLR)

2. 文本合成图片 文本向量-->特征-->图片 就是找对应关系

✅ [Generative Adversarial Text to Image Synthesis] [Paper][Code][code]

3.更快的方式训练gan

✅ [Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)

✅ [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code]

✅ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code]

✅ [Improved Training of Wasserstein GANs] [Paper][Code]

✅ [Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow] [Paper][Code]

✅ [Progressive Growing of GANs for Improved Quality, Stability, and Variation ] [Paper][Code][Tensorflow Code]

Image blending

1.提升画质

✅ [GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper][Code]

Image Inpainting

✅ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code](CVPR 2017)

✅ [Context Encoders: Feature Learning by Inpainting] [Paper][Code]

✅ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]

✅ [Generative face completion] [Paper][code](CVPR2017)

✅ [Globally and Locally Consistent Image Completion] [MainPAGE](SIGGRAPH 2017)

Object Detection

✅ [Perceptual generative adversarial networks for small object detection] [Paper](CVPR 2017)

✅ [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [Paper][code](CVPR2017)

Image translation

✅ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [Paper][Code]

✅ [Image-to-image translation using conditional adversarial nets] [Paper][Code][Code]

✅ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [Paper][Code]

✅ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [Paper][Code]

✅ [CoGAN: Coupled Generative Adversarial Networks] [Paper][Code](NIPS 2016)

✅ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper](NIPS 2017)

✅ [Unsupervised Image-to-Image Translation Networks] [Paper]

✅ [Triangle Generative Adversarial Networks] [Paper]

✅ [ST-GAN: Unsupervised Facial Image Semantic Transformation Using Generative Adversarial Networks] [Paper]

✅ [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] [Paper][code]

✅ [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings] [Paper](Reviewed)

✅ [UNIT: UNsupervised Image-to-image Translation Networks] [Paper][Code](NIPS 2017)

✅ [Toward Multimodal Image-to-Image Translation] [Paper][Code](NIPS 2017)

✅ [Multimodal Unsupervised Image-to-Image Translation] [Paper][Code]

Facial Attribute Manipulation

✅ [Autoencoding beyond pixels using a learned similarity metric] [Paper][code][Tensorflow code]

✅ [Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code]（NIPS）

✅ [Invertible Conditional GANs for image editing] [Paper][Code]

✅ [Learning Residual Images for Face Attribute Manipulation] [Paper][code](CVPR 2017)

✅ [Neural Photo Editing with Introspective Adversarial Networks] [Paper][Code](ICLR 2017)

✅ [Neural Face Editing with Intrinsic Image Disentangling] [Paper](CVPR 2017)

✅ [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] [Paper](BMVC 2017)[code]

✅ [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [Paper](ICCV 2017)

✅ [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] [Paper][code](CVPR 2018)

✅ [LEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes ] [Paper][code]

工程

1.部署环境 1-2d

2.开发第一个程序 mnist 1-2d 了解流程

3.学习gan，给出方案 2-3d

4.获取训练数据，训练模型，验证效果 4-5d

5.提升方案 10d