<commit_before># *Feed-Forward-Style-Transfer* implemented in TensorFlow

<img src="lib/images/examples/cover.jpg" height="480px" width="640px" align="right">

This is a TensorFlow implementation of *[Perceptual Losses for Real-Time Style Transfer
and Super-Resolution](http://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16.pdf)* using [instance normalization]() as a regularizer to improve training efficiency and test results.<sup>[1](#footnote1)</sup>

The loss derived from *[A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf)* is used to train a generative neural network to apply artistic style transfer to some input image. [Total variation denoising](http://eeweb.poly.edu/iselesni/lecture_notes/TVDmm/TVDmm.pdf) is used as a regularizer to reduce unrealistic constrast between adjacent pixels, which in turn reduces visible noise. 

Unlike the non-feed-forward implementation, this implementation is deterministic due to the nature of the generator.

#### Implementation Architecture

A generative convolutional neural network was used with downsampling layers followed by [residual blocks](https://arxiv.org/abs/1512.03385) then upsampling by [deconvolutional](https://en.wikipedia.org/wiki/Deconvolution) layers. Normalization of inputs is performed to reducing internal covariate shift.

For description of the generator's output, a pretrained [VGG network](https://arxiv.org/pdf/1409.1556.pdf) was used. It is provided [here](https://github.com/machrisaa/tensorflow-vgg) by [machrisaa](https://github.com/machrisaa) on GitHub. The VGG implementation was customized to accomodate the implementation requirements requirements and is of the 16-layer variety.
<commit_msg>updated README to mention fractionally strided convolution<commit_after># *Feed-Forward-Style-Transfer* implemented in TensorFlow

<img src="lib/images/examples/cover.jpg" height="480px" width="640px" align="right">

This is a TensorFlow implementation of *[Perceptual Losses for Real-Time Style Transfer
and Super-Resolution](http://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16.pdf)* using [instance normalization]() as a regularizer to improve training efficiency and test results.<sup>[1](#footnote1)</sup>

The loss derived from *[A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf)* is used to train a generative neural network to apply artistic style transfer to some input image. [Total variation denoising](http://eeweb.poly.edu/iselesni/lecture_notes/TVDmm/TVDmm.pdf) is used as a regularizer to reduce unrealistic constrast between adjacent pixels, which in turn reduces visible noise. 

Unlike the non-feed-forward implementation, this implementation is deterministic due to the nature of the generator.

#### Implementation Architecture

A generative convolutional neural network was used with downsampling layers followed by [residual blocks](https://arxiv.org/abs/1512.03385). This is followed by upsampling layers accomplished using [fractionally strided convolutions](https://arxiv.org/pdf/1605.06211v1.pdf), which has been incorrectly termed "deconvolution" in some publications. Normalization of inputs is performed to reducing internal covariate shift.

For description of the generator's output, a pretrained [VGG network](https://arxiv.org/pdf/1409.1556.pdf) was used. It is provided [here](https://github.com/machrisaa/tensorflow-vgg) by [machrisaa](https://github.com/machrisaa) on GitHub. The VGG implementation was customized to accomodate the implementation requirements requirements and is of the 16-layer variety.

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