Training Generative Adversarial Networks with Limited Data

Understand

Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge.

• We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes.
• The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset.
• We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images.