Deep Learning
Generative Adversarial Networks — AI Creates Art, Faces, and More
Learn how GANs use adversarial training to generate realistic synthetic data from noise.
- Adversarial training — generator vs. discriminator dynamic
- Image synthesis — create photorealistic faces and art
- Training dynamics — Nash equilibrium, mode collapse, and convergence
Creativity is intelligence having fun.
GANs — Generative Adversarial Networks
GANs (Goodfellow et al., 2014) learn to generate data by framing generation as a two-player minimax game between a generator and discriminator .
GAN Architecture
How GAN training works: The diagram shows the two-player game at the heart of GANs. The Generator (green, left) takes random noise z (a 128-512 dimensional vector sampled from a normal distribution) and transforms it through deconvolution layers into a fake image. The Discriminator (yellow, right) receives both real images from the dataset and fake images from the generator, outputting a probability D(x) ∈ [0,1] — real images should score near 1, fake near 0. The training dynamics box at the bottom explains the adversarial loop: the generator learns from gradients flowing back through the discriminator ("make more realistic images"), while the discriminator learns to distinguish better ("catch the fakes"). At equilibrium, the generator produces perfect samples and the discriminator outputs 0.5 for everything — it can no longer tell real from fake. This minimax game drives both networks to improve simultaneously.
Loss Functions
Training Process
Mode Collapse
DCGAN Architecture
GAN Variants
WGAN (Wasserstein GAN)
Key Takeaways
What to Learn Next
-> Autoencoders Learn about compressed representations.
-> Variational Autoencoders Generate data with probabilistic models.
-> Diffusion Models Deep Dive Master modern generative AI techniques.
-> Neural Networks Understand the foundation of deep learning.
-> CNNs Learn the convolutional architectures used in GANs.
-> Training Deep Networks Master training techniques for unstable models.