DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering

Zeya Wang, Yang Ni, Baoyu Jing, Deqing Wang, Hao Zhang, Eric Xing

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this article, we propose a Bayesian nonparametric framework, deep nonparametric Bayes (DNB), for jointly learning image clusters and deep representations in a doubly unsupervised manner. In doubly unsupervised learning, we are dealing with the problem of 'unknown unknowns,' where we estimate not only the unknown image labels but also the unknown number of labels as well. The proposed algorithm alternates between generating a potentially unbounded number of clusters in the forward pass and learning the deep networks in the backward pass. With the help of the Dirichlet process mixtures, the proposed method is able to partition the latent representations space without specifying the number of clusters a priori. An important feature of this work is that all the estimation is realized with an end-to-end solution, which is very different from the methods that rely on post hoc analysis to select the number of clusters. Another key idea in this article is to provide a principled solution to the problem of 'trivial solution' for deep clustering, which has not been much studied in the current literature. With extensive experiments on benchmark datasets, we show that our doubly unsupervised method achieves good clustering performance and outperforms many other unsupervised image clustering methods.

Original languageEnglish
Pages (from-to)7610-7620
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
StatePublished - Dec 1 2022

Bibliographical note

Funding Information:
The work of Yang Ni was supported in part by the National Science Foundation (NSF) under Grant DMS-1918851.

Publisher Copyright:
© 2012 IEEE.


  • Bayesian nonparametrics (BNPs)
  • convolutional neural network (CNN)
  • image clustering
  • joint learning
  • regularization

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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