Generative Adversarial Networks (GANs) are a type of unsupervised machine learning algorithm where two submodels, a generator and a discriminator, compete against each other. The generator creates fake samples, while the discriminator attempts to distinguish between real samples from a domain and fake samples from the generator. The adversarial nature of GANs lies in this competition. The generator iteratively creates samples, updating its model until it can generate samples convincing enough to fool both the discriminator and humans. Both the generator and discriminator receive feedback on their performance, and the loser updates its model accordingly. This process continues until the generator becomes so proficient that the discriminator can no longer identify its fakes. While often used in image generation, GANs have various applications, including video frame prediction, image enhancement, and encryption.

Ver todos los episodios