lyttonhao / Neural-Style-MMD

MXNet Code For Demystifying Neural Style Transfer (IJCAI 2017)

Neural-Style-MMD

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]

Introduction

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.

Prerequisites

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

Usage

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.