DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder

Zhenshan Bing, Yuan Meng, Yuqi Yun, Hang Su, Xiaojie Su, Kai Huang, Alois Knoll

Keywords: Unsupervised representation learning, Generative model, Bayes non-parametrics, deep clustering, variational auto-encoder, incremental features

TL;DR: A non-parametric generative model that enable infinite clustering from stream of unknown number features, outperforms existing parametric and non-parametric sota baselines in over six benchmark datasets, including STL-10 and ImageNet.

Abstract:

Generative model-based deep clustering frameworks excel in classifying complex data, but their effectiveness is limited when dealing with dynamically changing features due to their reliance on 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 **D**irichlet Process Mixtures based **I**ncremental deep clustering framework via **V**ariational **A**uto-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.

Primary Area: generative models

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Submission Number: 347