  Training GANs under limited data often leads to discriminator overfitting and
memorization issues, causing divergent training. Existing approaches mitigate
the overfitting by employing data augmentations, model regularization, or
attention mechanisms. However, they ignore the frequency bias of GANs and take
poor consideration towards frequency information, especially high-frequency
signals that contain rich details. To fully utilize the frequency information
of limited data, this paper proposes FreGAN, which raises the model's frequency
awareness and draws more attention to producing high-frequency signals,
facilitating high-quality generation. In addition to exploiting both real and
generated images' frequency information, we also involve the frequency signals
of real images as a self-supervised constraint, which alleviates the GAN
disequilibrium and encourages the generator to synthesize adequate rather than
arbitrary frequency signals. Extensive results demonstrate the superiority and
effectiveness of our FreGAN in ameliorating generation quality in the low-data
regime (especially when training data is less than 100). Besides, FreGAN can be
seamlessly applied to existing regularization and attention mechanism models to
further boost the performance.
