Post by account_disabled on Mar 11, 2024 1:18:43 GMT -5
GAN networks can generate content (images, videos, music) in an incredibly realistic way, and to do so they use a principle that I find fascinating and astonishing... in a certain sense they compete and challenge each other, playing cops and robbers. Let's find out how they work with practical examples. Alessio Pomaro Alessio Pomaro Aug 17, 2022 •9 min read Artificial intelligence playing cops and robbers to create and predict: let's discover GAN networks Artificial intelligence playing cops and robbers to create and predict: let's discover GAN networks More and more often, we hear about content generation through artificial intelligence . For example, we now know GPT-3 and various algorithms that allow us to create images starting from a natural language description.
Today, I would like to delve into generative systems whose operation is nothing India Mobile Number Data short of fascinating and astounding. I am referring to GAN networks . What are GANs? GANs ( Generative Adversarial Networks ) are algorithmic architectures that exploit two neural networks by contrasting them with each other with the aim of generating new data potentially very similar to real data. They are often used in the generation of images, videos, music and voices, but also for much more. And we'll find out soon. How do GANs work? Let's try to understand in a simple way how these systems work.
The two neural networks are called " generator " and " discriminator ", and metaphorically, we can say that they play cop and thief with each other. The thief ( generator ), in fact, creates " paintings " with the aim of convincing the guard ( discriminator ) that it is an authentic work. And he will try until the guard falls into the trap. Clearly, the protagonists of the game continually train: the thief to produce paintings that are ever closer to reality , the guard to grasp more and more details so as not to be " cheated ". So let's try, for example, to apply the metaphor to the generation of images of human faces. Both networks will be trained by processing millions of images in line with those that the algorithm will have to produce.
Today, I would like to delve into generative systems whose operation is nothing India Mobile Number Data short of fascinating and astounding. I am referring to GAN networks . What are GANs? GANs ( Generative Adversarial Networks ) are algorithmic architectures that exploit two neural networks by contrasting them with each other with the aim of generating new data potentially very similar to real data. They are often used in the generation of images, videos, music and voices, but also for much more. And we'll find out soon. How do GANs work? Let's try to understand in a simple way how these systems work.
The two neural networks are called " generator " and " discriminator ", and metaphorically, we can say that they play cop and thief with each other. The thief ( generator ), in fact, creates " paintings " with the aim of convincing the guard ( discriminator ) that it is an authentic work. And he will try until the guard falls into the trap. Clearly, the protagonists of the game continually train: the thief to produce paintings that are ever closer to reality , the guard to grasp more and more details so as not to be " cheated ". So let's try, for example, to apply the metaphor to the generation of images of human faces. Both networks will be trained by processing millions of images in line with those that the algorithm will have to produce.