🎨 GANs Introduction
Explore Generative Adversarial Networks and adversarial learning
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0 / 5 completedWhat are GANs?
🎯 Generative Adversarial Networks
GANs are a revolutionary deep learning architecture introduced by Ian Goodfellow in 2014. They consist of two neural networks—a Generator and a Discriminator—competing against each other in a game-theoretic framework.
The generator tries to create fake data that looks real, while the discriminator tries to distinguish fake from real. Through this competition, both networks improve.
Generator (G)
Creates fake samples from random noise, trying to fool the discriminator.
- •Input: Random noise vector (latent space)
- •Output: Generated data (images, text, etc.)
- •Goal: Maximize discriminator error
Discriminator (D)
Classifies samples as real or fake, acting as a critic for the generator.
- •Input: Real or generated data
- •Output: Probability (real vs fake)
- •Goal: Correctly classify all samples
🚀 Why GANs Matter
Create photorealistic images, faces, artwork, and deepfakes
Transform images between different artistic styles
Generate synthetic training data for ML models
📈 Historical Impact
Since 2014, GANs have revolutionized generative AI. They've enabled applications from creating realistic human faces (StyleGAN) to generating high-resolution images (BigGAN) and even artistic creations that sell for thousands of dollars.