Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features. We released our source code implementation at: https://github.com/Ghiara/diva

翻译：基于生成模型的深度聚类框架在处理复杂数据分类方面表现优异，但在处理动态复杂特征时存在局限性，因其需要预先知道聚类数。本文提出一种非参数深度聚类框架，采用高斯无限混合模型作为先验。该框架利用记忆化在线变分推断方法，实现聚类的"诞生"与"合并"操作，从而能以"动态自适应"方式对数据进行聚类，无需预先知道特征数量。我们将该框架命名为DIVA——基于狄利克雷过程的增量式深度聚类框架（通过变分自编码器实现）。该框架性能超越现有最优基线模型，在动态特征变化的复杂数据分类中展现出卓越性能，尤其在处理增量特征时表现突出。我们已开源代码实现于：https://github.com/Ghiara/diva