Style Transfer with Tensorflow

A Neural Algorithm of Artistic Style” is an accessible and intriguing paper about the distinction and separability of image content and image style using convolutional neural networks (CNNs). In this post we’ll explain the paper and then run a few of our own experiments.

To begin, consider van Gogh’s “The Starry Night”:

1280px-van_gogh_-_starry_night_-_google_art_project

The content of this picture refers to things like a tree in the foreground, the spired church, the small houses at the foot of the hills, stars, the moon, etc. while the style would refer to, well, van Gogh’s style: dark outlines around objects, almost uniformly thick brushstrokes across the entire image regardless of distance, swirls, the color palette bounded by indigos and blues and soft yellows, etc.

If you’re familiar with CNNs, you’re aware that extracting the content from an image is fairly straightforward because most computer vision applications you’ve worked on are likely concerned with identifying and labeling the content of an image. With a deep CNN we train kernels for low level features like edge detection and high level features like identification of eyes – all content-related features.

We can even take a trained net and examine an individual neuron’s “responsibility” with the network by setting the neighboring layer neuron gradients to 0 and running backpropagation:

Screen Shot 2017-06-12 at 11.59.40 AM

(From Justin’s Johnson’s presentation in Stanford CS20: Tensorflow for Deep Learning Research)

Extracting style without content from an image is a less obvious task, and is the main innovation of the paper, which they then put to good use by combining the style from one image with the content of another.

Screen Shot 2017-06-12 at 12.21.22 PM

Cool, huh?

Given how well this works to create rich style representations, extrapolation of style from an image is surprisingly simple. Basically, we want to look at correlations between the different filters applied at a given layer. To do this we pass the filter over the image to create the feature maps at our layer, and then we compute the inner product between feature maps of a given layer. The inner product is called the Gram matrix (it’s literally just a matrix dot its transpose), and gives us some intuition about extracting style: if entry ij in our Gram matrix has a high value, the two feature maps activated simultaneously on that portion of the style image. Looking at the set of all Gram matrices should tell us something about the abstracted style of the image.

Here are style reconstructions from “The Starry Night.” a) is the style reconstruction from the lowest layer in our CNN, while each successive style reconstruction combines a higher layer along with all of the previous ones; e) is a combination of the highest CNN layer and all previous ones.

Screen Shot 2017-06-12 at 12.45.24 PM

These make sense: in a) we’re seeing the color palette from our 3 channels, and not much else since the lowest CNN filters will be very simple things like edge detection, so their inner product will be a messy and pixelated. As more layers are added and higher-level filters are applied, the style increases in scale and we see more complex “macro-features” of the style that layers deeper in the network will pick up like swirls, wisps, color concentrations (yellow lunar objects) without necessarily seeing any content from the original image.

In order to create hybrid images, the authors devise a novel method of combining style loss and content loss from two images, then perform gradient descent on a white noise image to match the style and content from the respective input images. This is run on a modified version of a pre-trained CNN called VGGNet used for image classification since we need effective pre-trained filters in our network. (VGGNet uses 16 convolutional layers, 5 pooling layers – authors discard fully connected layer and use average pooling instead of max pooling for better results). Our white noise image is fed through the trained CNN and loss is a combination of style and content; each training iteration brings us closer to the style of our style image and the content of our content image.

For a given layer l the content loss is just squared error loss between our original content image and our white noise image.

L_{content} = \frac{1}{2} \sum_{i,j} (F^{l}_{ij} - P^{l}_{ij})^2

where

F^{l}_{ij}= the feature maps from N filters, and F_{ij} is the activation of filter i at position j in our white noise image

P^{l}_{ij} = the feature maps from N filters, and P_{ij} is the activation of filter i at position j in our original content image

Gradient descent will penalize our white noise image for diverging from our content image, and weights will be updated such that our white noise image comes to resemble our content image.

For the style loss in a given layer l, we need to compute all (N choose 2) possible Gram matrices from our input style image with N feature maps, then compute the Gram matrices from our white noise image, then take the mean square error (with some coefficients). Then, since we’re probably interested in a combined subset of convolution layers for our style representation (recall the different style scales in “The Starry Night” above), we take a weighted sum of those layers:

L_{l} = \frac{1}{4N^{2}_{l}4M^{2}_{l}} \sum_{i,j} (G^{l}_{ij} - A^{l}_{ij})^2

L_{style} = \sum_{l=0}^{L} w_{l} L_{l}

where

A^{l}_{ij}‘s = the Gram matrices in our original style image

G^{l}_{ij}‘s = the Gram matrices in our white noise style image

N = the number of feature maps in our layer

M = the height times width of our feature maps

w = (fixed) weights assigned to style loss in each layer contributing to overall style loss

Finally, to get the total loss objective function we just combine style loss and content loss with weighting parameters:

L_{total} = \alpha L_{content} + \beta L_{style}

There are a lot of hyperparameters to tune like which style layers to include, what weights to give to each style layer, noising on the image to be generated, the alpha/beta ratio in the total loss function, not to mention plain old neural net things like learning rate and number of iterations. For this reason (and because it takes 20 minutes per image on my machine), my images won’t look as nice or tuned as those in the paper, but you’ll still get the idea and see how parameter changes affect results. An obvious parameter to change is the alpha/beta ratio to control more vs. less style. Inclusion, exclusion, and weighting of high and low layers for the style loss is another. Another interesting and less obvious parameter has to do with resizing the style image – your hybrid image will contain features at different scales depending on the size of your style image:

Screen Shot 2017-06-12 at 11.36.52 PM.png

If you want to run these yourself the code is available from Chip Huyen’s Tensorflow for Deep Learning course at Stanford, which I highly recommend if you’d like to get good at Tensorflow and see (read: steal) some high quality TF frameworks. It’s an assignment in the class, so it’s worth implementing the mathematical details of the paper yourself, though solutions are included.

There’s another implementation here which I haven’t tried. It’s less involved and uses a VGG implementation that’s much easier to handle, though in either case you will have to download a trained image classification network (.5 GB). On the downside it doesn’t look like it offers logging or examination with tensorboard:

Screen Shot 2017-06-11 at 10.33.26 PM.png

The Stanford code comes with some style images and a content image in case you want to see Deadpool mixed with Picasso:

deadpool.jpg

guernica.jpg

299 copy

 

 

 

I wanted to test my intuition on this, so gave the system some tasks I though it would struggle to combine:

peacock.jpg

business_people.jpg

299.png

As expected, the Beardsley image isn’t well suited to the mathematical definition of style as the Gram matrix. Beardsley’s “Peacock” has lots of isolated pockets with wildly different styles and the granularity of detail (and the uniformity of detail) doesn’t hold across the image. If you asked a human to combine these images, they would probably robe the business people engaged in the rather obscure/invisible tug-of-war with intricately decorated cloaks. In general, you want style images that have stylistic similarities across the image in scale and detail, which is why the style images used in the paper and successful style transfers tend to be impressionist or highly patterned.

I didn’t think it wouldn’t work, but couldn’t resist trying to Simpsonize a family on their couch:

family.jpg

299.png

A little cartoony, but also nightmare-fuel. Lots of little blue-haired Homers and one Leela (second on the left).

Let’s try some hyperparameter tuning on the following images to better understand style transfer.

Here’s van Gogh on a small village, with 1st and 299th iteration displayed.

van_gogh_bedroom.jpg

village.jpg

1v.png

159v.png

When running these pay attention to the loss functions in tensorboard as they might save you a lot of time. The majority of my images saw no significant qualitative change or change in total loss past the first 100 iterations.

Screen Shot 2017-06-11 at 10.33.57 PM.png

Here’s some hyperparameter tuning so you can get a better idea of what different layers contribute to the hybrid:

village.jpg

pattern.jpg

 

5 style layers used [‘conv1_1’, ‘conv2_1’, ‘conv3_1’, ‘conv4_1’, ‘conv5_1’]

199.png

2 style layers used [‘conv1_1’, ‘conv2_1’]. Note the smaller, tiled, less macroscopic features, as discussed above with style representations in “The Starry Night.”

199.png

1 style layer used [‘conv5_1’]. Here we see lots of more complex style features, in distinction to the image above.

199.png

 

 

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