Generative Adversarial Networks (GANs) are a class of machine learning frameworks that use two neural networks – a Generator and Discriminator – to create data that mimics real-world examples. Introduced by Ian Goodfellow in 2014, GANs have become a foundational tool for generative AI, particularly in the fields of image, video, and audio synthesis.
How GANs Work
The generator takes random noise as input, and tries to produce fake data (e.g., images) that resemble real training data.
The discriminator takes both real data and generated data, and learns to distinguish between real and fake.
Each trains simultaneously to generate more realistic product data while sharpening its ability to spot fakes with greater precision.
GAN Applications
Create realistic faces, art, logos, and designs.
Turn sketches into photorealistic images.
Animate still photos or replicate speech.
GAN Limitations and Challenges
Training Instability: GANs are notoriously hard to train – they may diverge or collapse if one network outpaces the other.
No Semantic Understanding: GANs don't "understand" what they generate – they just learn patterns.
Misuse Risk: Powerful tool for deepfakes, misinformation, and synthetic media manipulation.
