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.
翻译:本文为有条件的GAN培训引入了一个新颖且完全不受监督的框架,在这个框架中,标签自动从数据中获取。我们将一个集群网络纳入一个标准的有条件的GAN框架,这个网络对歧视者起反作用。它与生成者一起,旨在寻找一个共同的结构化绘图,将假标签与真实和假图像联系起来。我们的发电机在FID方面优于无条件的GAN,在图像网和LSUN等大型数据集上有很大的差幅。它也优于在CIFAR10和CIFAR100上培训的关于人类标签的有条件GAN级,在这两个地方没有精细的注释或每类大量样品。此外,我们的集群网络超过了CIFAR100集群方面的最新技术。