Keras implementation of "A Neural Algorithm of Artistic Style"
An implementation of the arXiv preprint
A Neural Algorithm of Artistic Style [1]
& paper
Image Style Transfer Using Convolutional Neural Networks [2].
Supports TensorFlow 2.4.1.
style-transfer.ipynb describes the style transfer
process between a white noise image x, a content image p, and a style
representation a. Performing gradient descent of the content loss and
style loss with respect to x impressions the content of p into x,
bearing local styles, and colors from a.
| Original Photograph Tubingen, Germany |  | |
| Claude Monet Houses of Parliament |  |  |
| Pablo Picasso Seated Nude |  |  |
| Edvard Munch The Scream |  |  |
| Vincent van Gogh The Starry Night |  |  |
| William Turner The Shipwreck of The Minotaur |  |  |
| Wassily Kandinsky Composition VII |  |  |
content-reconstruction.ipynb describes the
content reconstruction process from white noise. Performing gradient descent
of the content loss on a white noise input x for a given content p
yields a representation of the networks activation for a given layer l.
| Layer | Result |
|---|---|
block1_conv1 |  |
block2_conv1 |  |
block3_conv1 |  |
block4_conv1 |  |
block4_conv2 |  |
block5_conv1 |  |
style-reconstruction.ipynb describes the style
reconstruction process on Wassily Kandinsky’s Composition VII from white
noise. Performing gradient descent of the style loss on a white noise input
x for a given artwork a yields a representation of the networks
activation for a given set of layers L.
| Layer | Result |
|---|---|
block1_conv1 |  |
block1_conv1, block2_conv1 |  |
block1_conv1, block2_conv1, block3_conv1 |  |
block1_conv1, block2_conv1, block3_conv1, block4_conv1 |  |
block1_conv1, block2_conv1, block3_conv1, block4_conv1, block5_conv1 |  |
content-layer.ipynb visualizes how the style transfer
is affected by using different layers for content loss.
| Layer | Result |
|---|---|
block1_conv1 |  |
block2_conv1 |  |
block3_conv1 |  |
block4_conv1 |  |
block5_conv1 |  |
style-layers.ipynb visualizes how the style transfer is
affected by using different sets of layers for style loss.
| Layers | Result |
|---|---|
block1_conv1 |  |
block1_conv1, block2_conv1 |  |
block1_conv1, block2_conv1, block3_conv1 |  |
block1_conv1, block2_conv1, block3_conv1, block4_conv1 |  |
block1_conv1, block2_conv1, block3_conv1, block4_conv1, block5_conv1 |  |
optimizers.ipynb employs gradient descent, adam, and
L-BFGS to understand the effect of different black-box optimizers. Gatys et.
al use L-BFGS, but Adam appears to produce comparable results without as much
overhead.
| Gradient Descent | Adam | L-BFGS |
|---|---|---|
 |  |  |
tv-loss.ipynb introduces total-variation loss to reduce
impulse noise in the images.
| TV Loss Scale Factor | Result |
|---|---|
0 |  |
1 |  |
10 |  |
100 |  |
1000 |  |
photo-realistic-style-transfer.ipynb
describes the photo-realistic style transfer process. Opposed to transferring
style from an artwork, this notebook explores transferring a nighttime style
from a picture of Piedmont Park at night to a daytime picture of Piedmont Park.
| Content | Style | Result |
|---|---|---|
 |  |  |