Generative Adversarial Networks
What are deep fakes? How are they generated? On today’s episode of Big Data Big Questions we tackle how Generative Adversarial Networks work. Generative Adversarial Networks or GANs work with 2 neural networks one a generator and another a discriminator. Learn about my experience with GANs and how you can build one as well.
Transcript What Is A Generative Adversarial Network?
This is going to be a cool episode, Mr. Editor. We’re going to talk about a painting that was built by AI or designed by AI that went for over $400,000. Crazy.
Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today, we’re going to talk about Generative Adversarial Neural Networks. We’re going to talk about a painting, so you’ve all probably heard about a painting that was sold for, like, $400,000. It was built, actually, but a Generative Adversarial Network. We’re going to talk about that, explain what that is, and maybe even look at a little bit of code, and tell you how you can learn more about it.
Before we jump in, I definitely want to say, if you have any questions about data engineering, data science, IT, anything, put them in the comment section here below. Reach out to me at thomashenson.com/big-questions. I’ll try my best to answer them and help you out. It’s all about the community and all about having fun. Today, we’re going to have a lot of fun. I’m excited. This is something that I’ve been researching and looking into since, maybe, at least since the first part of 2019, but for sure it’s been a theme for me for a while.
I want to talk about Generative Adversarial Network, what that is. We think about that from a deep learning perspective. We’ve done some videos. We talk about deep learning, but this is a specific kind, so kind of like [Inaudible 00:01:33] neural networks, this is a little bit different. It still uses the premise of, you have your input layer, you have your hidden layers, and you have your output layer, but it’s a little more complexity to it. It’s been around since 2014. Ian Goodfellow is branded as the creator to that. If you follow Andrew Neen [Phonetic 00:01:52] on Twitter, I just saw where he took a role at Facebook. I think it was a competitive thing, and I think Andrew was saying, “Hey, great pickup for Facebook for picking him up,” but you might want to fact check that.
Like I said, that was breaking news here. Generative Adversarial Network. The way that I like to think about that and describe that is, think of it as having two different neural networks that are working. You have your discriminator and you have your generator. What’s going on is your generator is taking data. Think of, we’ve got, let’s say, a whole bunch of images of people. What’s going on is, our generator is going to take that data set and look at it, and it’s going to try to create fake data that looks like real data. Your discriminator is the one that’s sitting there saying, “Hey, wait a minute. That’s real data. This is fake data.” This is real data, that’s fake data. Just continuing on. You keep going through that iteration, until the generator gets so good, he’s able to pass fake data onto the discriminator. For our example, we’re looking at images of people. What you’re trying to do is, you’re trying to generate data of fake people and pass it through as real people. You’re probably like, “Man. How really good is that?”
Check out this website here. These are fake people. These are not real people. These are really good images, and a little bit creepy. I found this, actually, in the last week, and kind of looked at it. Been sharing it internally with some friends and some colleagues, but man. It’s really interesting when you think about it. These people do not exist. There’s no, these people don’t exist on the planet. These were all built by AI or deep learning. It’s pretty cool. Pretty creepy, too.
You’re probably wondering, “That’s pretty cool.” Been around since 2014. I’m researching it. Should I be researching it? I definitely think it’s something that’s going to be out there. There’s a lot of information around it, and a lot of use cases, kind of don’t know where it’s going to go. I can think of it being used for game development. Being able to create worlds. For somebody that’s creating a game that’s going to have multiple, multiple different levels, or even if GIS, you have to create all these landscapes and everything like that. If you can build AI to automate that, if you use a deep learning algorithm that’s going to automate, and build out those worlds, and make them lifelike, how much busy work is that going to save you? Same thing with GIS and in architecture, but also go back to the website we were just looking at, with the fake people. Oh, my gosh! You can use that in media and entertainment. Think about movies. Maybe we don’t even need actors anymore. That’s a little bit scary. For the actors, I don’t know. You still need Thomas Henson and thomashenson.com on YouTube, right?
Really cool. Something I just wanted to share with everybody, and back to what we were talking about in the first part of the show. The first art that was really sold for big ticket item around AI, over $400,000, and it was a generated image, too. I talk a little bit about it in my implementing TF Learn course, but here’s a code sample, really just showing what’s going on. If you’re looking at it, and all this is done in TensorFlow, here, using the extraction layer of TF Learn. Look here, how we’re creating that generator, and how you’re creating a discriminator. It’s a good bit of code here, but really, this is an example from TF Learn examples, where you’re actually starting to general data in here. It’s pretty cool. Pretty awesome to be able to play with if you have Tensorflow installed in your environment. You can actually do an import TF learn and start running this code from the examples here, and start tweaking with it. Really cool.
I you want to learn more, definitely love for you to check out and tell me all about. Go through my TF Learn course. Tell me all about it if you like it. You don’t have to, but I just thought sharing Generative Adversarial Networks, I thought that was pretty cool. I think it’s something that everybody should learn. At least know a little bit about it. Now, you know. Hey, important thing. I’ve got my generator. I’ve got my discriminator. My generator is making the data that’s trying to pass this real data to my discriminator.
Boom! You understand a lot. Thanks for tuning in. If you have any questions, put them in the comment section here below, and make sure you subscribe just so you never miss an episode, and get some great education around Big Data Big Questions.
Nobody can! Nobody can generate a fake image of me!