mit-han-lab/data-efficient-gans

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1308 stars · 173 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of Differentiable Augmentation (DiffAugment), a NeurIPS 2020 method that improves GAN training data efficiency by applying differentiable augmentations to both real and generated samples. It includes StyleGAN2 and BigGAN variants in PyTorch and TensorFlow for low-shot and limited-data image generation.

Use cases

• train a GAN with only 100 images
• generate images from a small dataset without pre-training
• improve GAN training stability with limited data
• reproduce StyleGAN2 results on CIFAR-10 with less data
• apply differentiable augmentation to GAN training
• train BigGAN on ImageNet with TPU

When to choose

• you have very few training images (100-1000) and want high-fidelity GAN generation
• you want a research-validated augmentation method compatible with StyleGAN2 or BigGAN
• you need both PyTorch and TensorFlow implementations

When to avoid

• you need diffusion models or modern generative approaches instead of GANs
• you have abundant training data where standard GANs already work well
• you need actively maintained production software rather than research code

Facets

library · maturity maintenance

machine-learning image-processing deep-learning deep-learning computer-vision image-processing artificial-intelligence python cross-platform gan diffaugment stylegan2 biggan data-augmentation image-generation pytorch tensorflow research-code neurips-2020 gpu linux

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