Generative Adversarial Networks (GANs) are a type of neural network consisting of two models: a generator and a discriminator. They are trained together in a process of competition, where the generator tries to create realistic data and the discriminator evaluates whether the data is real or generated.
How GANs work:
- Generator: Creates fake data that mimics real data.
- Discriminator: Evaluates whether the data is real or fake.
- Training: The generator and discriminator are trained together. The generator tries to fool the discriminator, while the discriminator tries to correctly classify the data.
Example applications of GANs:
- Generating realistic images or videos.
- Creating new artistic styles.
- Denoising images.
GANs are a powerful tool in the field of artificial intelligence, enabling the creation of realistic data based on training examples.
