What are generative adversarial networks (GANs)?

Generative adversarial networks (GANs) are indeed a framework where two models compete against each other. This innovative approach involves a generator model, which creates synthetic data, and a discriminator model, which evaluates the authenticity of the data produced, distinguishing between real and generated samples. The generator aims to produce data that is indistinguishable from real data, while the discriminator improves its ability to differentiate between the two. This adversarial process drives both models to enhance their performance over time, leading to high-quality data generation.

This competitive dynamic is at the core of how GANs operate and is fundamental to their success in various applications, including image generation, video creation, and text synthesis. The iterative process where one model tries to outsmart the other fosters continuous improvement, making GANs a powerful tool in the realm of artificial intelligence and machine learning.

The other options do not accurately capture the essence of GANs. For instance, describing GANs as a single neural network for image processing misses the crucial aspect of the two competing networks. Similarly, classifying them as a type of supervised learning algorithm or a collection of regression techniques does not reflect their unique generative and adversarial characteristics.