  Training generative adversarial networks (GANs) with limited data is
challenging because the discriminator is prone to overfitting. Previously
proposed differentiable augmentation demonstrates improved data efficiency of
training GANs. However, the augmentation implicitly introduces undesired
invariance to augmentation for the discriminator since it ignores the change of
semantics in the label space caused by data transformation, which may limit the
representation learning ability of the discriminator and ultimately affect the
generative modeling performance of the generator. To mitigate the negative
impact of invariance while inheriting the benefits of data augmentation, we
propose a novel augmentation-aware self-supervised discriminator that predicts
the augmentation parameter of the augmented data. Particularly, the prediction
targets of real data and generated data are required to be distinguished since
they are different during training. We further encourage the generator to
adversarially learn from the self-supervised discriminator by generating
augmentation-predictable real and not fake data. This formulation connects the
learning objective of the generator and the arithmetic $-$ harmonic mean
divergence under certain assumptions. We compare our method with
state-of-the-art (SOTA) methods using the class-conditional BigGAN and
unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100,
FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate
significant improvements of our method over SOTA methods in training
data-efficient GANs.
