ClusterGAN: Latent Space Clustering in Generative Adversarial Networks

0x00 Abstract

While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space.
In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we are able to achieve clustering in the latent space.
Our results show a remarkable phenomenon that GANs can preserve latent space interpolation across cate- gories, even though the discriminator is never exposed to such vectors.

Vanilla GAN does not cluster well in the latent space!