  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
