mlsanityData to 7 Oct 2026

Training Generative Adversarial Networks with Limited Data

arXiv 2006.06676 ↗First posted 11 Jun 2020cs.CV cs.LG cs.NEcited by 1,184

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Cited by 202 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 1,184 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.

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Citing papers per year: 2021: 194; 2022: 300; 2023: 316; 2024: 197; 2025: 108; 2026: 69.

1,184 papers.

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38 more
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