Clustering is a challenging and crucial task in unsupervised learning. Recently, though many clustering algorithms combined with deep learning have been proposed, we observe that the existing deep clustering algorithms do not considerably preserve the clustering structure and information of raw data in the learned latent space. To address this issue, we propose a Generative Adversarial Attention Clustering network Based on Inverse autoencoder (IAE-ClusterGAN), which can control the distribution type of the learned latent code without additional constraints so that unsupervised clustering tasks can be done efficiently. Meanwhile, we integrate the attention mechanism into the network to make the latent code contain more useful clustering information. Moreover, we utilize hyperspherical mapping in the discriminator to improve the stability of model training and reduce the training parameters. Experimental results demonstrate that IAE-ClusterGAN achieves competitive results compared to the state-of-the-art models on five benchmark datasets.

聚类是无监督学习中一项具有挑战性且至关重要的任务。最近，虽然已经提出了许多结合深度学习的聚类算法，但我们观察到现有的深度聚类算法并没有在学习的潜在空间中大量保留原始数据的聚类结构和信息。为了解决这个问题，我们提出了一种基于逆自动编码器的生成对抗注意力聚类网络（IAE-ClusterGAN），它可以在没有额外约束的情况下控制学习到的潜在代码的分布类型，从而可以有效地完成无监督的聚类任务。同时，我们将注意力机制集成到网络中，使潜在代码包含更多有用的聚类信息。而且，我们在判别器中利用超球面映射来提高模型训练的稳定性并减少训练参数。实验结果表明，与五个基准数据集上的最新模型相比，IAE-ClusterGAN 取得了有竞争力的结果。