Generative Adversarial Networks (GANs) are a class of neural networks that generate new data with similar statistics to real-world data. They consist of two networks: a generator and a discriminator. The generator tries to create data that looks like the real data, while the discriminator tries to distinguish between real data and generated data.
Key Concepts
- Generator: The generator network creates new data instances that are indistinguishable from real data.
- Discriminator: The discriminator network evaluates the generated data and determines whether it is real or fake.
- Training Process: The generator and discriminator are trained simultaneously in a competitive manner. The generator tries to fool the discriminator, while the discriminator tries to improve its ability to distinguish between real and generated data.
Step-by-Step Guide
- Initialize the Generator and Discriminator: Both networks are initialized randomly.
- Train the Discriminator: Present the discriminator with both real and generated data. The discriminator is trained to distinguish between the two.
- Train the Generator: After training the discriminator, the generator is updated. The generator is given feedback from the discriminator to improve its output.
- Iterate: Repeat the training process for a large number of iterations to refine the generator's output.
Example Code
# This is a simplified example of a GAN in Python using TensorFlow.
# For detailed implementation, refer to the following link: [GAN Implementation](/en/learn/tutorials/gan/implementation)
Resources
Images
GAN Architecture
GAN Training Process