GANs Generative Models

Generative Adversarial Networks (GANs) are a deep learning architecture for generating new, synthetic data. Two neural networks compete: a Generator (creates fake images/text) and a Discriminator (judges real vs fake). The Generator learns to fool the Discriminator; the Discriminator learns to catch the Generator. Through iteration, the Generator becomes skilled at producing realistic data. Common uses: image synthesis (generating realistic faces, photorealistic scenes), style transfer (painting in artist's style), data augmentation (creating more training data), and deepfake detection. Modern applications include Stable Diffusion, DALL-E, and text-to-image models.