论文标题
改善gan的一致性正则化
Improved Consistency Regularization for GANs
论文作者
论文摘要
最近的工作通过对歧视者的一致性成本来提高生成对抗网络（GAN）的性能。我们通过多种方式改进了这一技术。我们首先表明，一致性正则化可以将工件引入gan样品中，并解释如何解决此问题。然后，我们对旨在提高其性能的一致性正则化程序进行了几次修改。我们进行了广泛的实验，以量化我们改进的好处。对于Cifar-10和Celeba上的无条件图像综合，我们的修改在各种GAN架构上获得了最著名的FID分数。对于CIFAR-10上的条件图像合成，我们将最新的FID得分从11.48提高到9.21。最后，在Imagenet-2012上，我们将技术应用于原始的BigGan型号，并将FID从6.66提高到5.38，这是该模型大小时最佳分数。
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance. We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID from 6.66 to 5.38, which is the best score at that model size.
