This repository holds the MXNet code for the paper

Demystifying Neural Style Transfer, Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou, International Joint Conference on Artificial Intelligence (IJCAI), 2017

[Arxiv Preprint]

Neural-Style-MMD presents a neural style transfer algorithm based on a new interpretation. Instead of using Gram matrix in original neural style transfer methods, this repo provides two methods to implement style transfer, including a Maximum Mean Discrepancy (MMD) loss and a Batch Normalization (BN) statistic loss. The paper also demonstrates the original matching Gram matrix is equivalent to the a specific polynomial MMD. Details could be found in the paper. Our implementation is based on the neural-style example of MXNet.

Before running this code, you should make the following preparations:

• Install MXNet following the instructions and install the python interface. This repo is tested on commmit 01cde1.

• Download the pre-trained VGG-19 model in the model folder:

wget https://github.com/dmlc/web-data/raw/master/mxnet/neural-style/model/vgg19.params

Basic Usage:

python neural-style.py --mmd-kernel linear --gpu 0 --style-weight 5.0 --content-image input/brad_pitt.jpg --style-image input/starry_night.jpg --output brad_pitt-starry_night --output-folder output_images

We support 4 single transfer methods, including 3 mmd kernels, including linear, poly and Gaussian, and a BN Statistics Matching method. At the same time, the code supports fusing different transfer methods with specific weights.

Options

• --mmd-kernel: Specify MMD kernel (linear, poly, Gaussian), also their combination, e.g. linear,poly.
• --bn-loss: Whether to use the BN method.
• --multi-weights: The weights when fusing different transfer methods, e.g. 0.5,0.5.
• --style-weight: How much to weight the style loss term. It is equivalent to the balance factor gamma in the paper when we fix the content-weight as 1.0.

You can run python neural-style.py with -h to see more options.