Hi and welcome to our lecture on generative adversarial neural networks, or Gans for short.
So let's get started.
So what's in store in this section?
Well, firstly, I'll tell you what guns are and why they're so exciting.
I'll give you some examples of guns that have been put out there by researchers over the years.
I'll tell you how guns work and then how you can go about treating your own guns, as well as some of
the challenges you will experience.
And then I can give you some practical implementations of guns because they may not be immediately obvious
to you.
Why guns are so useful.
And here we have some images of some gun outputs.
You can see Google's big gun.
This is this one is actually generating fake images here that look so real, as well as first generation
performed by Guns Hill.
You can see all of these faces A.I. generated faces.
They're not real people.
So it's quite amazing, isn't it?
So let's talk about what are guns?
So guns were first introduced in 2014 by Ian Goodfellow.
Guns are a type of neural network that what they do, they generate data that could have plausibly come
from an existing distribution of symbols.
So let's take a look at what that means.
Here we have a sample of the endless dataset here, and in this column, we have data that has been
generated by a gun that looks like it came from the embassy, they said.
But it wasn't there.
This has been artificially generated by a gun.
Same old faces.
Same with some images from the sapphire dataset as well.
And for a technology, council actually said guns were one of the best innovations of the last three
years.
This was in 2018.
So guns are quite exciting and quite promising, so we can take a look at some of the examples of guns
over the years.
This one came out in 2018.
It's called Big Gun, and it was able to generate all of these artificially generated images here.
Very cool.
We can even do anime characters, which is a project will be doing in discourse later on.
Generate some anime characters.
So we give it a dataset of existing anime characters and we start generating new ones out of it.
Next, we can look at something called image to image translation, where we have an input image here.
This is the ground truth as well here for a night scene, and it generates this other dusk looking scene
here, which is an image of these two.
Similar to this one, you can basically ticket image that's daytime and then generate a night vision
of that image based on this type of data set.
Also, this one called sketches two images, so you can just feed again a sketch like this, and it
creates an output like this so they can create a person, can create a backpack, you can create a show,
as we can see here.
Pretty awesome, isn't it?
Next, we can text to image translations, so give it some ticks.
The small bird has a redhead with feathers that feed from red to gray, from head to tail, and it generates
images of a bird that fits that description.
That is extremely impressive, in my opinion, and this is another example of it here.
Also, we have semantic image to photo translation.
So later on in discourse, you will look at things that segmentation models that do segmentation for
a scene so we can separate to cause him to ruin and the people and trees as built in buildings as well.
Well, what if we were to tweet you were to take this image here?
This segmented image here and generate photorealistic scene out of it?
That's what these scans do.
The reverse engineer, that's semantic representation and very popular one which I have used in real
life production scenarios many times.
That's cool.
That's super resolution, Garneau.
So again, for short, you can take a lower resolution image here.
So what if you were using a CCTV camera that was getting a blurry feed of see a license plate you can
use as Horrigan to enhance that image and then pass it to you will see our and it probably will improve
by a few percentage points in accuracy.
That's what I've experienced, in my opinion.
So next, we take a look at how guns work, so stay tuned for that lesson.
Thank you for watching.