Deep clustering which adopts deep neural networks to obtain optimal
representations for clustering has been widely studied recently. In this paper,
we propose a novel deep image clustering framework to learn a category-style
latent representation in which the category information is disentangled from
image style and can be directly used as the cluster assignment. To achieve this
goal, mutual information maximization is applied to embed relevant information
in the latent representation. Moreover, augmentation-invariant loss is employed
to disentangle the representation into category part and style part. Last but
not least, a prior distribution is imposed on the latent representation to
ensure the elements of the category vector can be used as the probabilities
over clusters. Comprehensive experiments demonstrate that the proposed approach
outperforms state-of-the-art methods significantly on five public datasets.

本文提出了一种新颖的基于深度学习的图像聚类框架，通过最大化互信息和使用不变性损失来学习一个分类 - 风格的潜在表征，将图像的类别信息与其样式信息分离，并将类别向量作为聚类分配的概率。实验结果表明，与现有方法相比，该方法在五个公共数据集上的表现显著优于其他方法。