Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér.
Style transfer is a mostly AI-based technique
where we take a photograph, put a painting
next to it, and it applies the style of the
painting to our photo.
A key insight of this new work is that a style
is complex and it can only be approximated
with one image.
One image is just one instance of a style,
not the style itself.
Have a look here - if we take this content
image, and use Van Gogh's "Road with Cypress
and Star" painting as the art style, we get
this.
However, if we would have used Starry Night
instead, it would have resulted in this.
This is not learning about a style, this is
learning a specific instance of a style!
Here you see two previous algorithms that
were instead, trained on a collection of works
from Van Gogh.
However, you see that they are a little blurry
and lack detail.
This new technique is able to address this
really well - also, look at how convincingly
it stylized the top silhouettes of the bell
tower.
It can also deal with HD videos at a reasonable
speed of 9 of these images per second.
Very tasty, love it!
And of course, as style transfer is a rapidly
growing field, there are ample comparisons
in the paper against other competing techniques.
The results are very convincing - I feel that
in most cases, it represents the art style
really well and can decide where to leave
the image content similar to the input and
where to apply the style so the overall outlook
of the image remains similar.
So we can look at these results and discuss
who likes which one all day long, but there
are also other, more objective ways of evaluating
such an algorithm.
What is really cool is that the technique
was tested by human art history experts, and
they not only found this method to be the
most convincing of all the other style transfer
methods, but also thought that the AI-produced
paintings were from an artist 39% of the time.
So this means that the algorithm is able to
learn the essence of an artistic style from
a collection of images.
This is a huge leap forward.
Make sure to have a look at the paper that
also describes a new style-aware loss function
and differences in the training process of
this method as well.
And, if you enjoyed this episode and would
like to see more, please help us exist through
Patreon.
In this website, you can support the series
and pick up cool perks like early access to
these videos, deciding the order of future
episodes, and more.
You know the drill, a dollar a month is almost
nothing, but it keeps the papers coming.
We also support cryptocurrencies, you'll find
more information about this in the video description.
Thanks for watching and for your generous
support, and I'll see you next time!
