Spatial attention GAN for unsupervised clustering

ZhengYu Xiao; Fei Chen

doi:10.1117/12.2589354

27 January 2021 Spatial attention GAN for unsupervised clustering

ZhengYu Xiao, Fei Chen

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117202F (2021) https://doi.org/10.1117/12.2589354
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Clustering has been a hot research topic in unsupervised learning. Recently, the Generative Adversarial Networks (GANs) has achieved good results in clustering tasks such as ClusterGAN. However, this type of model could not work well on class imbalanced data. In this paper, the spatial attention and class balanced term are adopted to improve the data clustering. The proposed Spatial Attention GAN (SAGAN) can effectively rebalance the feature maps and achieve more reliable clustering when the number of samples in the dataset for each class is not balanced. Experiments show the promising results and the potential of the method for unsupervised clustering.

Citation Download Citation

ZhengYu Xiao and Fei Chen "Spatial attention GAN for unsupervised clustering", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117202F (27 January 2021); https://doi.org/10.1117/12.2589354

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