  Generative Adversarial Networks (GANs) have achieved great success in
generating realistic images. Most of these are conditional models, although
acquisition of class labels is expensive and time-consuming in practice. To
reduce the dependence on labeled data, we propose an un-conditional generative
adversarial model, called K-Means-GAN (KM-GAN), which incorporates the idea of
updating centers in K-Means into GANs. Specifically, we redesign the framework
of GANs by applying K-Means on the features extracted from the discriminator.
With obtained labels from K-Means, we propose new objective functions from the
perspective of deep metric learning (DML). Distinct from previous works, the
discriminator is treated as a feature extractor rather than a classifier in
KM-GAN, meanwhile utilization of K-Means makes features of the discriminator
more representative. Experiments are conducted on various datasets, such as
MNIST, Fashion-10, CIFAR-10 and CelebA, and show that the quality of samples
generated by KM-GAN is comparable to some conditional generative adversarial
models.
