参考
http://hacker.duanshishi.com/?p=1693
http://blog.csdn.net/hungryof/article/details/53981959
http://blog.csdn.net/hungryof/article/details/61195783
http://blog.csdn.net/wyl1987527/article/details/70245214
https://www.ctolib.com/AdaIN-style.html
https://www.leiphone.com/news/201705/ro8EB2entk3vXybM.html(Styles 历史)
http://www.jianshu.com/p/b1189448eb2e
http://blog.csdn.net/wspba/article/details/53994649
Acknowledgement
This project is inspired by many existing style transfer methods and their open-source implementations, including:
- Image Style Transfer Using Convolutional Neural Networks, Gatys et al. [code (by Johnson)]
- A Neural Algorithm of Artistic Style, （原版）
- Texture Synthesis Using Convolutional Neural Networks
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images(改进1)
- Instance Normalization The Missing Ingredient for Fast Stylization
- Deep Identity-aware Transfer of Facial Attributes
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson et al. [code]（改进2）
- Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis, Ulyanov et al. [code]
- A Learned Representation For Artistic Style, Dumoulin et al. [code]
- Fast Patch-based Style Transfer of Arbitrary Style, Chen and Schmidt [code] （超越fast style transfer----任意风格图和内容图0.1秒出结果) https://github.com/rtqichen/style-swap
- Controlling Perceptual Factors in Neural Style Transfer, Gatys et al. [code]
- Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization (2017 -- 更快速度) [code]
- deep-photo-styletransfer-master [code]