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.

翻译：在有限数据下训练生成对抗网络（GANs）具有挑战性，因为判别器容易过拟合。先前提出的可微分增强方法展现了训练GANs时数据效率的提升。然而，该增强隐式地为判别器引入了对增强的不可取不变性，因为它忽略了数据变换导致的标签空间中语义变化，这可能限制判别器的表征学习能力，并最终影响生成器的生成建模性能。为了在继承数据增强优势的同时缓解不变性的负面影响，我们提出了一种新颖的增强感知自监督判别器，用于预测增强数据的增强参数。特别地，由于真实数据和生成数据在训练过程中存在差异，需要区分它们的预测目标。我们进一步鼓励生成器通过生成增强可预测的真实数据而非虚假数据，以对抗性方式从自监督判别器中学习。在特定假设下，该公式将生成器的学习目标与算术-调和均值散度联系起来。我们将所提方法与使用类条件BigGAN和无条件StyleGAN2架构的最先进方法在数据受限的CIFAR-10、CIFAR-100、FFHQ、LSUN-Cat及五个小样本数据集上进行了比较。实验结果表明，在训练数据高效GANs时，我们的方法相较于最先进方法有显著改进。