Generative Adversarial Networks (GANs)

Definition

GANs are a class of machine learning models where two neural networks—a generator and a discriminator—compete to create realistic synthetic data.

Purpose

The purpose is to generate realistic data such as images, audio, or text. GANs are used in creative industries, data augmentation, and research.

Importance

• Produces high-quality synthetic data.
• Enables creativity in design and art.
• Risk of misuse for deepfakes and misinformation.
• Computationally expensive to train.

How It Works

1. Generator creates synthetic data from random noise.
2. Discriminator evaluates if data is real or fake.
3. Both networks are trained simultaneously.
4. Generator improves by learning to fool discriminator.
5. Iteration continues until outputs resemble real data.

Examples (Real World)

• NVIDIA StyleGAN: generates realistic human faces.
• DeepFake applications: synthetic video creation.
• Synthetic medical images for research data augmentation.

References / Further Reading

• Goodfellow et al. “Generative Adversarial Nets.” NeurIPS 2014.
• Ian Goodfellow’s GAN Lecture Notes.
• IEEE Transactions on Neural Networks and Learning Systems.