  Generative adversarial networks (GANs) have shown outstanding performance on
a wide range of problems in computer vision, graphics, and machine learning,
but often require numerous training data and heavy computational resources. To
tackle this issue, several methods introduce a transfer learning technique in
GAN training. They, however, are either prone to overfitting or limited to
learning small distribution shifts. In this paper, we show that simple
fine-tuning of GANs with frozen lower layers of the discriminator performs
surprisingly well. This simple baseline, FreezeD, significantly outperforms
previous techniques used in both unconditional and conditional GANs. We
demonstrate the consistent effect using StyleGAN and SNGAN-projection
architectures on several datasets of Animal Face, Anime Face, Oxford Flower,
CUB-200-2011, and Caltech-256 datasets. The code and results are available at
https://github.com/sangwoomo/FreezeD.
