STYLE TRANSFER What is style transfer Style transfer
- Slides: 11
STYLE TRANSFER 风格迁移 李克钹
What is style transfer? Style transfer is the technique of recomposing images in the style of other images. What can it do?
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History of Style Transfer • There exists some non-parametric algorithms, but they are limited to inform the texture transfer with only low-level image features of the target image. • Gatys introduced a way to use Convolutional Neural Network (CNN) to separate and recombine the image content and style of natural images by extracting image representations from response layers in VGG networks. • Later Johnson proposed a way to train a feed-forward network to approximate the operation. This technique was used by Prisma, an app that let users render any image with the chosen artistic style.
How to realize Style Transfer ▪ Visualize data ▪ Basic Preprocessing/preparing our data ▪ Set up loss functions ▪ Create model ▪ Optimize for loss function
Algorithms of Style Transfer • Slow Neural Method • Parametric Slow Neural Method • Non-parametric Slow Neural Method • Fast Neural Method • • • Per-Style-Per-Model(PSPM) Multiple-Style-Per-Model(PSPM) Arbitrary-Style-Per-Model(ASPM)
Some interesting application • Text Style Transfer + • Face Aging • Video Style Transfer(《Come Swim 》) • 2 D-to-3 D Style Transfer
reference • 陈淑環, 韦玉科, 徐乐, 董晓华, 温坤哲. 基于深度学习的图像风格迁移研究综述[J/OL]. 2019, 36(8). [2018 -07 -23]. http: //www. arocmag. com/article/02 -2019 -08 -063. html. • Palsson S , Agustsson E , Timofte R , et al. Generative Adversarial Style Transfer Networks for Face Aging[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society, 2018. • Kato H , Ushiku Y , Harada T. Neural 3 D Mesh Renderer[J]. 2017. • Gatys L A , Ecker A S , Bethge M. Image Style Transfer Using Convolutional Neural Networks[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. • https: //golden. com/wiki/Image_Style_Transfer • https: //www. jiqizhixin. com/articles/2018 -05 -15 -5
Thanks 李克钹
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