We introduce a simple but effective unsupervised method for generating realistic and diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator's feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both image diversity and standard quality metrics, compared to previous methods.

翻译：我们引入了一个简单而有效的、不受监督的方法来生成现实和多样化的图像。 我们不使用手动加注类标签来培训等级条件的GAN模型。 相反, 我们的模型以从歧视者特征空间的集群中自动产生的标签为条件。 我们的集群步骤自动发现多种模式,并明确要求生成者覆盖这些模式。 标准模式崩溃基准实验显示,我们的方法在处理模式崩溃时优于几种竞争方法。 我们的方法在图像网络和Places365等大型数据集上也表现良好, 与以往的方法相比,改善了图像多样性和标准质量衡量标准。