This is a project of XJTLU 2017 Summer Undergraduate Research Fellowship, it aims at designing a generative adversarial network to implement style transfer from a style image to content image. Related literature could be viewed from Wiki
Neural Style Transfer is one of the cutting-edge topic in deep learning field. Given an colored image, like this proposed, and another image that contains the style desired, they could be combined by using Neural Style Transfer and it looks like this.
Our goal is to implement the neural style transfer by using cycleGAN. At the same time, we also want to take one step further by using CAN, which could generate image itself after a well-feed training process.
Despite so many existing and well-performed deep learning frameworks (like caffe, chainer etc), our group chooses Tensorflow for its reliability and adaptability.
Edge detection based on Kears deep learning framework has been implemented, and test image is
There are more results have released by using Keras framework, please see this [link](http://stellarcoder.com/surf/anime_test) created by DexHunter. The network is trained on Professor Flemming 's workstation with 4 Titan X GPUs, which cost 2 weeks to implement.
This file is essential for the network, the download link could be viewed from here
- The first trial is using traditional neural style transfer, the model is pretrained VGG-19 network and there is no generator and discriminator. The network is just a simple CNN with 16 2d convolution layers, 5 average pooling layers and 3 fc layers. Obviously, when the iteration is 100, the output is crap. However, this crap cost me 1 hour to build with cpu i7-6700HQ.
Well, the second one seems better
This result is obtained from the network after 1000 iterations, and it is trained on 4 Titan GPUs. But I think this result is not comfortable. Development and investigation are still needed.