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Deep Image Clustering with Category-Style Representation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12359)

Abstract

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 (Project address: https://github.com/sKamiJ/DCCS).

Keywords

Image clustering Deep learning Unsupervised learning 

Notes

Acknowledgements

This work was supported by National Key Research and Development Program of China (No. 2018AAA0100100), National Natural Science Foundation of China (61702095), the Key Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), National Key Research and Development Project (2018YFC2000702), Science and Technology Program of Shenzhen, China (No. ZDSYS201802021814180) and the Big Data Computing Center of Southeast University.

Supplementary material

504468_1_En_4_MOESM1_ESM.pdf (437 kb)
Supplementary material 1 (pdf 437 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Tencent Jarvis LabShenzhenChina

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