What is Generative Adversarial Network (GAN)?
Generative Adversarial Network (GAN) — A class of ML frameworks consisting of two neural networks contesting with each other to generate realistic data.
GANs pit two neural networks against each other: a generator that creates fake data and a discriminator that tries to distinguish fake from real. Through this competition, the generator learns to produce increasingly realistic outputs. GANs revolutionized image generation before diffusion models.
Frequently Asked Questions
Are GANs still used now that diffusion models exist?
Less for image generation, but GANs remain valuable for data augmentation, super-resolution, style transfer, and applications requiring real-time generation where diffusion models are too slow.
Why are GANs difficult to train?
The two networks must stay balanced. If the discriminator becomes too strong, the generator cannot learn. If too weak, the generator produces low-quality outputs. This instability requires careful hyperparameter tuning.
What are deepfakes?
Deepfakes use GAN technology to generate realistic fake videos of people. This raises serious ethical concerns around misinformation, consent, and identity theft.