Generative Adversarial Networks (GAN) have shown to be extremely powerful in producing amazing generative models that can make music, write poetry and even generating incredibly real-looking faces. GANs have been very popular in the past few years, since it was introduced by Ian Goodfellow in 2014. I believe one of the main factors contributing to GAN's popularity (besides its effectiveness duh) is the simplicity and intuitiveness of its design. The training process can be thought of as "a competition between counterfeiters and police," Goodfellow said. "Counterfeiters want to make fake money and have it look real, and police want to look at any particular bill and determine if it's fake."

I explain Artificial Intelligence terms and news to non-experts. Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won't believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution!

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities. One of the hottest topics in deep learning is GANs, which have the potential to create systems that learn more with less help from humans. Rather than train a single neural network to recognize pictures, researchers train two competing networks. The sparring networks learn from each other. As one works hard to find fake images, for example, the other gets better at creating fakes that are indistinguishable from the originals.