Generative Adversarial Networks (GANs) are a class of deep neural networks that are used for generative tasks, such as generating images, music, or text. GANs consist of two main components: a generator and a discriminator.
Key Components of GANs
Generator: The generator tries to create data instances that could pass as real data. It takes a random noise vector as input and generates a data sample.
Discriminator: The discriminator tries to distinguish between real data samples and fake data samples generated by the generator. It takes either a real data sample or a fake data sample as input and outputs a probability.
How GANs Work
- Initialization: Both the generator and the discriminator are initialized with random weights.
- Training Loop:
- The generator creates a fake data sample.
- The discriminator receives the fake data sample and outputs a probability of it being real.
- The generator is trained to improve the fake data samples to fool the discriminator.
- The discriminator is trained to better distinguish between real and fake data.
- Iteration: The process repeats for many iterations until the generator can produce realistic data samples.
Example of GAN Application
GANs have been successfully used to generate realistic images of human faces, animals, and even landscapes. One popular GAN for generating images is the CycleGAN.
For more information on GANs and their applications, you can read our detailed tutorial on GAN Applications.