What are generative adversarial networks (GANs)?

Generative adversarial networks (GANs) are indeed a class of frameworks where two neural networks compete to generate new data. This setup consists of a generator network, which creates new data instances, and a discriminator network, which evaluates whether the instances are genuine or generated. The generator tries to produce data that is indistinguishable from real data, while the discriminator works to improve its ability to tell the difference between real and fake data. This adversarial process continues until the generator creates data that is so realistic that the discriminator can no longer tell the difference effectively.

This competitive nature between the two networks is what makes GANs particularly powerful for generating high-quality images, videos, and other types of data that can be creatively synthesized. The learning process leads to improved performance over time for both networks, ultimately resulting in high-fidelity outputs. The innovation of GANs has spurred significant advancements in the field of artificial intelligence and has found applications in various areas such as art generation, image editing, and data augmentation.