GANs are a class of deep learning algorithms used to generate new data that resembles existing data. They consist of two main components: a Generator and a Discriminator.
Key Concepts
- Generator: Creates synthetic data (e.g., images, text) by learning patterns from real data.
- Discriminator: Evaluates whether data is real or generated, acting as a critic.
- Adversarial Training: The two networks compete in a game-theoretic framework to improve generation quality.
Applications
- 🖼️ Image Generation: Create realistic images from scratch (e.g., Style_Transfer).
- 🎨 Style Transfer: Replicate artistic styles onto new images.
- 🧠 Data Augmentation: Generate additional training samples for machine learning models.
Resources
For deeper exploration, check out our Variational Autoencoders course to compare GANs with other generative models.