Generative Adversarial Networks (GANs) - DALL-E Tutorial

- Another renowned generative AI model is generative adversarial networks, also referred as simply GANs. To illustrate how GANs work, let's give a game of forgery as a metaphor. Imagine you have an artist called The Generator who is trying to recreate a painting that is so realistic that it looks like a famous painting. And you have another person called The Discriminator who's an art expert and trying to spot the difference between the real painting and the forgery. The Generator creates a painting and The Discriminator evaluates it, giving feedback to the generator on how to improve the next iteration. The Generator and The Discriminator played this game repeatedly until The Generator creates the painting that is so realistic that The Discriminator can't tell the difference between it and the real painting. In the same way, a GAN model has a generator and a discriminator. The two parts work together in a competition. That's why it's called generative adversarial networks. In this way, they improve the generator's ability to create realistic data, and over time, the generator becomes better and better at creating realistic data. And the results start yielding in the creation of products, assets, faces, people, that didn't exist before, just like we have seen with text-to-image that we have seen in the former session. The difference though is that with GANs, you input one type of data, like pictures or bank transitions, and then you output the same type of data. Let's now give three real-world examples where GANs were used. We're going to start with Audi. They trained their own GANs to get inspiration for their wheel designs. This process created lots of different wheel designs that simply didn't exist before, and gave inspiration to Audi designers so they can pick and choose which designs they wanted to use in their final decisions. And remember, AI didn't design the final wheel. AI was simply a tool that the wheel designers used to inspire themselves for the final designs that they would make. Next, Beko, that is a European-based appliance brand, they use custom trained GANs in their sustainability stand film, which also happens to be the world's first brand-funded AI film created and produced by Seyhan Lee. We use GANs to generate lightning, leaves, roots, eyes, flowers, and created seamless transitions to flow between humans and nature. GANs have this beautiful transitional quality. And finally, in the context of financial fraud detection, GAN models can be used to generate synthetic versions of fraudulent transactions, which can then be used to train a fraud detection model. You know what's really surprising with GANs is that the same generative AI model can be used for two very distinct professions. Here we are seeing some financial fraud detection solving and create a new tire styles for Audi. And then later on, the same AI model makes impossibly beautiful visual effects for film, and that versatility is the greatest power of GAN models.