To visualize a Generative Adversarial Network (GAN), I'll provide a high-level overview of its architecture and then create a diagram to represent it.
A GAN consists of two main components:
- Generator: This component generates new data instances that resemble the training data.
- Discriminator: This component evaluates the authenticity of the generated data, distinguishing between real and fake data.
The training process involves the generator trying to produce data that can fool the discriminator, while the discriminator tries to become better at distinguishing real data from fake data. This adversarial process continues until the generator produces data that is indistinguishable from real data.
Let's visualize this architecture.