# Neural Style Transfer
[Neural style transfer](https://arxiv.org/abs/1508.06576) is an algorithm that combines the content of one image with the style of another image using CNN. Given a content image and a style image, the goal is to generate a target image that minimizes the content difference with the content image and the style difference with the style image.
#### Content loss
To minimize the content difference, we forward propagate the content image and the target image to pretrained [VGGNet](https://arxiv.org/abs/1409.1556) respectively, and extract feature maps from multiple convolutional layers. Then, the target image is updated to minimize the [mean-squared error](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/neural_style_transfer/main.py#L81-L82) between the feature maps of the content image and its feature maps.
#### Style loss
As in computing the content loss, we forward propagate the style image and the target image to the VGGNet and extract convolutional feature maps. To generate a texture that matches the style of the style image, we update the target image by minimizing the mean-squared error between the Gram matrix of the style image and the Gram matrix of the target image (feature correlation minimization). See [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/neural_style_transfer/main.py#L84-L94) for how to compute the style loss.
## Usage
```bash
$ pip install -r requirements.txt
$ python main.py --content='png/content.png' --style='png/style.png'
```
## Results
The following is the result of applying variaous styles of artwork to Anne Hathaway's photograph.
