搜集的一些neural style style github source and website

https://github.com/jcjohnson/neural-style

https://github.com/yusuketomoto/chainer-fast-neuralstyle
https://github.com/manuelruder/artistic-videos
https://github.com/DmitryUlyanov/texture_nets
https://github.com/mbartoli/neural-animation
https://github.com/zerolocker/neural-style
https://github.com/AbdullahAlfaraj/neural-style-website
https://github.com/DylanAlloy/NeuralStyle-WebApp
https://github.com/OlavHN/fast-neural-style
https://github.com/chuanli11/CNNMRF
https://github.com/Teaonly/easyStyle
https://github.com/searchXiaoLai/paintmaster
https://github.com/naman14/neural-style-android
https://github.com/DmitryUlyanov/fast-neural-doodle
https://github.com/layumi/2016_Artist_Style
https://github.com/alireza-a/neural-style-webapp
https://github.com/layumi/2015_Face_Detection


https://github.com/hashbangCoder/Real-Time-Style-Transfer


VGG_ILSVRC_19_layers_deploy.prototxt
https://github.com/genekogan/CubistMirror
https://github.com/gafr/chainer-fast-neuralstyle-models
https://github.com/awentzonline/keras-rtst
https://github.com/suquark/neural-style-visualizer
https://github.com/anishathalye/neural-style
https://github.com/larspars/neural-style-video
https://github.com/titu1994/Neural-Style-Transfer
https://github.com/andersbll/neural_artistic_style


Neural-Style movie
https://github.com/zhaw/neural_style
https://github.com/Explee/neural_style


Performance result


We can see a x703 time slower in a tensorflow CPU non-quantized implementation versus the AWS GPU


InfrastructureTime
tensorflow GPU K420 AWS:~0.0329s (batching) 0.026s (looping)
tensorflow mac os:4.13s (batching) 3.31s (looping)
tensorflow mac os simulateur:5.86s (float32) 16.9 (quantized, this is weird)
tensorflow iphone 6s CPU:18.30s (float32)


Image size: 600x600
batching: all image are processed in parallel
looping: one image at a time
quantized: Using tensorflow quantization system, (Shouldn't be slower, probably needs a cleaner tf graph)


https://github.com/SaMnCo/docker-neuraltalk2
Place instances_train-val2014.zip in the same folder. You can download it from http://mscoco.org/dataset/#download
Then run the following commands:
Deep learning algorithm paints smooth-moving works of art


http://newatlas.com/neural-network-videos/44580/ ?PDF截圖
推特上一個(gè)藝術(shù)家收集整理的藝術(shù)化視頻資料集合
http://www.genekogan.com/works/style-transfer.html ?資料合集
https://gist.github.com/genekogan/d61c8010d470e1dbe15d 制作流程
http://www.kylemcdonald.net/stylestudies/ ?選擇合適的Style，不要無畏的浪費(fèi)GPU Time
?
https://m.youtube.com/watch?utm_campaign=buffer&utm_source=twitter.com&v=BuuNjnjpqFI&utm_medium=social&utm_content=buffer5220e
http://mt.sohu.com/20160816/n464537105.shtml
http://blog.josephmisiti.com/making-neural-art
https://github.com/mtyka/neural_artistic_style
?
用7臺(tái)手機(jī)測(cè)試。3個(gè)iPhone，4個(gè)Android。最快的android，9秒左右，最慢的ios，約15秒左右。


https://github.com/rupeshs/neuralstyler
https://github.com/ryankiros/neural-storyteller
https://github.com/gafr/chainer-fast-neuralstyle-models/issues/5
https://github.com/SergeyMorugin/ostagram
https://developer.apple.com/reference/accelerate/1912851-bnns
https://github.com/collinhundley/Swift-AI/issues/50
http://arxiv.org/abs/1603.08155
https://github.com/jcjohnson/neural-style/issues/313


https://github.com/yusuketomoto/chainer-fast-neuralstyle/issues/1#issuecomment-228944706
markz-nyc commented 23 hours ago
Prisma already made the neural style into offline mode, how iPhone can get such good result in few seconds without using gpu?
@DylanAlloyDylanAlloy commented 23 hours ago ? edited
Because it's based on C code probably rather than Python.


https://github.com/awentzonline/keras-rtst.git
https://www.researchgate.net/publication/301836893_Perceptual_Losses_for_Real-Time_Style_Transfer_and_Super-Resolution
https://vimeo.com/167910860
http://www.creativeai.net/posts/EyoFYu2ZDv6T3zdYi/real-time-style-transfer-with-keras
https://www.reddit.com/r/MachineLearning/comments/4cj1jj/160308155_perceptual_losses_for_realtime_style/
http://www.le.com/ptv/vplay/22792152.html
https://plus.google.com/+ResearchatGoogle/posts/KxetFFXpPTJ
http://www.academia.edu/254458/Algorithm_for_Real-Time_Style_Transfer_for_Human_Motion
http://www.slideshare.net/yusuketomoto/realtime-style-transfer-63669036
?
http://blog.csdn.net/lebula/article/details/51896836
論文筆記：Perceptual Losses for Real-Time Style Transfer and Super-Resolution[doing]
1.transformation: image to image?
2.perceptual losses:
? ? ?psnr是per-pixel的loss,值高未必代表圖片質(zhì)量好，廣泛應(yīng)用只是因?yàn)橛?jì)算比較簡(jiǎn)單
對(duì)圖片做super resolution
define 4 loss:
1.pixel L2 ?:feature reconstruction loss
2.gram matrix:style reconstruction loss
3.per-pixel
4.TV


http://www.genekogan.com/works/style-transfer.html
http://humanmotion.ict.ac.cn/papers/2015P1_StyleTransfer/details.htm
http://dl.acm.org/citation.cfm?id=2766999
https://www.versioneye.com/python/keras-rtst/0.0.1
pip install https://pypi.python.org/packages/source/k/keras-rtst/keras-rtst-0.0.1.tar.gz


https://blogs.nvidia.com/blog/2016/05/25/deep-learning-paints-videos/
http://www.meyumer.com/pdfs/SpectralStyleTransfer.pdf
http://blogs.scientificamerican.com/sa-visual/neural-networks-for-artists/
http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html
https://github.com/dmlc/mxnet/tree/master/example/neural-style
http://www.nerdcore.de/2016/07/10/prisma-style-transfer-kommt-fuer-android-und-die-technik-hinter-der-app/
http://link.springer.com/article/10.1007/s11554-016-0612-0
http://sssslide.com/www.slideshare.net/yusuketomoto/realtime-style-transfer-63669036
http://www.trancefish.de/blog/show/design/Prima+ist+auch+zum+Filmemachen+geeignet/
http://petapixel.com/2016/07/20/prisma-app-turns-standard-timelapse-incredible-moving-painting/
http://petapixel.com/2016/08/03/artisto-app-prisma-video-turns-videos-van-goghs/
https://blog.my.com/artisto-app-for-video-processing-when-your-videos-turn-into-paintings-that-come-to-life/
https://www.engadget.com/2016/08/03/artisto-prisma-for-videos/
?
style transfer in real-time
?
https://github.com/6o6o/chainer-fast-neuralstyle.git


#!/bin/bash
for name in ./*.jpg; do convert -resize 256x256\! $name $name; done


ffmpeg -i input.flv ?out%d.png
ffmpeg -i input.flv -vf fps=1 out%d.png
#ffmpeg -framerate 30 -i img%03d.png -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4
ffmpeg -framerate 30 -i input%03d.jpg -codec copy ?-pix_fmt yuv420p output.mkv

總結(jié)

以上是生活随笔為你收集整理的搜集的一些neural style style github source and website的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。