Spatial attention GAN for unsupervised clustering
Zhengyu Xiao, Fei Chen · 2021
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.