GAN (Generative Adversarial Network)
A deep learning framework that trains two networks - a generator and a discriminator - in an adversarial manner to produce images indistinguishable from real ones.
A GAN (Generative Adversarial Network) is a generative framework proposed by Goodfellow et al. in 2014. It pits a Generator against a Discriminator, driving both to improve through competition until generated images become indistinguishable from real data.
Training mechanism:
- Generator (G): Takes random noise z and produces synthetic images, aiming to fool the discriminator
- Discriminator (D): Binary classifier judging whether images are real or generated
- Minimax game: G maximizes D's error while D maximizes accuracy, driving improvement
- Nash equilibrium: Ideally converges to D outputting 0.5 for all inputs
Landmark architectures:
DCGAN(2015): First practical GAN using convolutional layersPix2Pix(2016): Conditional GAN for paired image translationCycleGAN(2017): Unpaired style transfer without matched dataStyleGAN(2018): Style-based generator for high-resolution facesStyleGAN3(2021): Eliminated aliasing for natural synthesis
Applications include super-resolution (SRGAN, ESRGAN), inpainting, and data augmentation. Training instability (mode collapse, vanishing gradients) is addressed by Wasserstein distance and spectral normalization. Diffusion models now lead in quality, but GANs retain speed advantages for real-time use.