Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
In the last few years, we have seen a bunch of new AI-based techniques that were specialized
in generating new and novel images.
This is mainly done through learning-based techniques, typically a Generative Adversarial
Network, a GAN in short, which is an architecture where a generator neural network creates new
images, and passes it to a discriminator network, which learns to distinguish real photos from
these fake, generated images.
These two networks learn and improve together, and generate better and better images over
time.
What you see here is a set of results created with the technique by the name CycleGAN.
This could even translate daytime into nighttime images, reimagine a picture of a horse as
if it were a zebra, and more.
We can also use it for style transfer, a problem where we have two input images, one for content,
and one for style, and as you see here, the output would be a nice mixture of the two.
However, if we use CycleGAN for this kind of style transfer, we’ll get something like
this.
The goal was to learn the style of a select set of famous illustrators of children’s
books by providing an input image with their work.
So, what do you think about the results?
Well, the style is indeed completely different, but the algorithm seems a little too heavy-handed
and did not leave the content itself intact.
Let’s have a look at another result with a previous technique.
Maybe this will do better.
This is DualGAN, which refers to a paper by the name Unsupervised dual learning for image-to-image
translation.
This uses two GANs to perform image translation, where one GAN learns to translate, for instance,
from day to night, while the other one learns the opposite, night to day translation.
This, among other advantages, makes things very efficient, but as you see here, in these
cases, it preserves the content of the image, but perhaps a little too much, because the
style itself does not appear too prominently in the output images.
So CycleGAN is good at transferring style, but a little less so for content, and DualGAN
is good at preserving the content, but sometimes adds too little of the style to the image.
And now, hold on to your papers, because this new technique by the name GANILLA offers us
these results.
The content is intact, checkmark, the style goes through really well, checkmark.
It preserves the content and transfers the style at the same time!
Excellent!
One of the many key reasons as to why this happens is the usage of skip connections,
which help preserve the content information as we travel deeper into the neural network.
So, finally, let’s put our money where our mouth is and take a bunch of illustrators,
marvel at their unique style, and then, apply it to photographs and see how the algorithm
stacks up against other previous works.
Wow.
I love these beautiful results.
These comparisons really show how good the new GANILLA technique is at preserving content.
And note that these are distinct artistic styles that are really difficult to reproduce,
even for humans.
It is truly amazing that we can perform such a thing algorithmically.
Don’t forget that the first style transfer paper appeared approximately 3-3.5 years ago,
and now, we have come a long-long way!
The pace of progress in machine learning research is truly stunning!
While we are looking at some more amazing results, this time around, only from GANILLA,
I will note that the authors also made a user study with 48 people who favored this against
previous techniques.
And, perhaps leaving the best for last, it can even draw in the style of Hayao Miyazaki.
I bet there are a bunch of Miyazaki fans watching, so let me know in the comments what you think
about these results!
What a time to be alive!
This episode has been supported by Weights & Biases.
In this post they show you how to easily iterate on models by visualizing and comparing experiments
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Weights & Biases provides tools to track your experiments in your deep learning projects.
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Thanks for watching and for your generous support, and I'll see you next time!