  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.
