In the 'Demystifying AI' series, we're taking a look at the technologies behind Adversary - how they work and how you can easily set up your own, even if you don't have anything to do with AI or even Tech. In this article, we're looking at Generative Adversarial Networks.

From a design perspective, my early and original results - made by an algorithm called DCGAN (one of the most basic versions of a GAN network) - looked the best. But at a resolution of under 200x200px, they were also extremely pixeled and low-res. Let's enhance.

Looking at the past year, the biggest innovation in the GAN space might have been the BigGAN architecture. The mega-architecture, trained on 512 TPUs in the space of two days, also includes a sweatshirt category. I had to look into it

I liked GANs for a while. So much, that I started writing about them in reputable publications before I even had my first job in the Machine Learning field. I trained them mostly on faces, trying to enter the Uncanny Valley,

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