Generative Adversarial Networks (GANs) consist of two neural networks, a generator and discriminator, that are trained simultaneously. The generator produces new data from random noise input to resemble real data, while the discriminator evaluates if data is real or generated. Through adversarial training, the generator improves at fooling the discriminator until generated data becomes indistinguishable from real data. GANs have been successful in image generation and other domains but training remains challenging.

1. Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a class of machine learning frameworks introduced by
Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks: the generator
and the discriminator, which are trained simultaneously through adversarial training.
1. **Generator**: This network generates new data instances, such as images, by taking random
noise as input and transforming it into a meaningful output that resembles real data. The
generator tries to produce data that is indistinguishable from real data.
2. **Discriminator**: The discriminator network acts as a binary classifier that evaluates the
authenticity of the generated data. It distinguishes between real data from the training set and
fake data produced by the generator.
The training process of GANs involves a competitive game between these two networks:
- Initially, the generator produces random samples, which are easily discernible as fake by the
discriminator.
- The discriminator is trained on a combination of real and fake data and learns to distinguish
between them.
- Simultaneously, the generator aims to improve its ability to produce more realistic samples by
fooling the discriminator.
- As training progresses, both networks improve their capabilities until the generator produces
data that is nearly indistinguishable from real data, and the discriminator cannot reliably
differentiate between the real and fake samples.
GANs have shown remarkable success in various domains, including image generation, style
transfer, text-to-image synthesis, and even generating music and human faces. They have also
spurred significant research and advancements in deep learning and generative modeling.
However, training GANs can be challenging and requires careful tuning of hyperparameters, as well
as managing issues like mode collapse (where the generator collapses to producing a limited
variety of samples) and instability during training.