Generative Adversarial networks (GANs) have obtained remarkable success in
many unsupervised learning tasks and unarguably, clustering is an important
unsupervised learning problem. 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 categories, even though the discriminator is never exposed
to such vectors. We compare our results with various clustering baselines and
demonstrate superior performance on both synthetic and real datasets.

本研究提出了 ClusterGAN 作为使用 GAN 进行聚类的新机制，通过从一热编码变量和连续变量的混合中采样潜变量，再结合一个特定于聚类的损失函数，与一个将数据投影到潜空间的逆网络共同训练，我们能够在潜空间中进行聚类。结果表明，GAN 可以保留类别之间的潜空间插值，即使鉴别器从未接触过这些向量，并且在合成和真实数据集上，我们的结果比各种聚类基线表现更好。