论文标题
带有类别式表示形式的深层图像聚类
Deep Image Clustering with Category-Style Representation
论文作者
论文摘要
最近对采用深层神经网络以获得聚类的最佳表示的深度聚类已被广泛研究。在本文中，我们提出了一个新颖的深层图像聚类框架，以学习类别风格的潜在表示，其中类别信息与图像样式分开，并可以直接用作群集分配。为了实现这一目标，将共同信息最大化应用于潜在表示中的相关信息。此外，增强不变损失用于将表示形式分为类别部分和样式部分。最后但并非最不重要的一点是，在潜在表示上施加了先前的分布，以确保可以将类别向量的元素用作簇的概率。全面的实验表明，所提出的方法在五个公共数据集上的表现明显优于最先进的方法。
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.
