Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
This is what we have been waiting for.
Earlier, we talked about a neural network that was able to describe in a full sentence
what we can see on an image.
And it had done a damn good job at that.
Then, we have talked about a technique that did something really crazy, the exact opposite:
we wrote a sentence, and it created new images according to that.
This is already incredible.
And we can create an algorithm like this by training not one, but two neural networks:
The first is the generative network, that creates millions of new images, and the discriminator
network judges whether these are real or fake images.
The generative network can improve its game based on the feedback and will create more
and more realistic looking images, while the discriminator network gets better and better
at telling real images from fake ones.
Like humans, this rivalry drives both neural networks towards perfecting their crafts.
This architecture is called a generative adversarial network.
It is also like the classical, evergoing arms race between criminals who create counterfeit
money and the government, which seeks to implement newer and newer measures to tell a real hundred
dollar bill from a fake one.
The previous generative adversarial networks were adept at creating new images, but due
to their limitations, their image outputs were the size of a stamp at best.
And we were wondering, how long until we get much higher resolution images from such a
system?
Well, I am delighted to say that apparently, within the same year.
In this work, a two-stage version of this architecture is proposed.
The stage 1 network is close to the generative adversarial network we described.
And most of the fun happens in the stage 2 network, that takes this rough, low resolution
image and the text description and is told to correct the defects of the previous output
and create a higher resolution version of it.
In the video, the input text description and the stage-1 results are shown, and building
on that, the higher resolution stage-2 images are presented.
And the results are... unreal.
There was a previous article and Two Minute Papers episode on the unreasonable effectiveness
of recurrent neural networks.
If that is unreasonable effectiveness, then what is this?
The rate of progress in machine learning research is unlike any other field I have ever seen.
I honestly can't believe what I am seeing here.
Dear Fellow Scholars, what you see might very well be history in the making.
Are there still faults in the results?
Of course there are.
Are they perfect?
No, they certainly aren't.
However, research is all about progress and it's almost never possible to go from 0 to
a 100% with one new revolutionary idea.
However, I am sure that in 2017, researchers will start working on generating full HD animations
with an improved version of this architecture.
Make sure to have a look at the paper, where the ideas, challenges, and possible solutions
are very clearly presented.
And for now, I need some time to digest these results.
Currently I feel like being dropped into the middle of a science-fiction movie.
And, this one will be our last video for this year.
We have had an amazing year with some incredible growth on the channel, way more of you Fellow
Scholars decided to come with us on our journey than I would have imagined.
Thank you so much for being a part of Two Minute Papers, we'll be continuing full steam
ahead next year, and for now, I wish you a Merry Christmas and happy holidays.
2016 was an amazing year for research, and 2017 will be even better.
Stay tuned!
Thanks for watching and for your generous support, and I'll see you next time!