论文标题
自标记的条件剂
Self-labeled Conditional GANs
论文作者
论文摘要
本文介绍了一个新颖且完全无监督的框架，用于有条件的GAN训练，其中从数据自动获得标签。我们将一个聚类网络纳入与歧视者相对的标准条件GAN框架中。使用发电机，它旨在找到一个共享的结构化映射，以将伪标签与真实图像相关联。我们的发电机在FID方面优于无条件的gan，并在ImageNet和LSUN等大型数据集上具有明显的利润。它还超过了在CIFAR10和CIFAR100上的人类标签上训练的有条件的gans gans，在该标签上，细粒度的注释或每个班级都不可用。此外，我们的聚类网络超过了CIFAR100聚类的最新网络。
This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data. We incorporate a clustering network into the standard conditional GAN framework that plays against the discriminator. With the generator, it aims to find a shared structured mapping for associating pseudo-labels with the real and fake images. Our generator outperforms unconditional GANs in terms of FID with significant margins on large scale datasets like ImageNet and LSUN. It also outperforms class conditional GANs trained on human labels on CIFAR10 and CIFAR100 where fine-grained annotations or a large number of samples per class are not available. Additionally, our clustering network exceeds the state-of-the-art on CIFAR100 clustering.
