Training Generative Adversarial Networks with Limited Data

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. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42.

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Datasets


Introduced in the Paper:

MetFaces

Used in the Paper:

CIFAR-10 FFHQ AFHQ ArtBench-10 (32x32)
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image Generation AFHQ Cat StyleGAN2-ADA clean-KID 0.71 ± .02 # 2
clean-FID 3.28 ± .02 # 2
FID 3.55 # 4
Image Generation AFHQ Dog StyleGAN2-ADA clean-KID 1.28 ± .02 # 2
clean-FID 7.61 ± .02 # 2
FID 7.41 # 4
Image Generation AFHQ Wild StyleGAN2-ADA clean-KID 0.44 ± .01 # 2
clean-FID 3.00 ± .01 # 2
FID 3.05 # 4
Conditional Image Generation ArtBench-10 (32x32) StyleGAN2 + ADA FID 2.625 # 1
10-shot image generation Babies TGAN + ADA FID 97.91 # 6
Conditional Image Generation CIFAR-10 StyleGAN2-ADA Inception score 10.14 # 3
FID 2.42 # 3
Image Generation FFHQ 1024 x 1024 StyleGAN2 ADA+bCR FID 3.62 # 10
Image Generation FFHQ 256 x 256 StyleGAN2 + ADA (DINOv2) FD 514.78 # 8
Precision 0.59 # 9
Recall 0.06 # 10
Image Generation FFHQ 256 x 256 StyleGAN2 + ADA FID 3.62 # 14
Image Generation Pokemon 256x256 StyleGAN2-ADA FID 40.38 # 3

Methods