Clustering is one of the fundamental problems in unsupervised learning. Recent deep learning based methods focus on learning clustering oriented representations. Among those methods, Variational Deep Embedding achieves great success in various clustering tasks by specifying a Gaussian Mixture prior to the latent space. However, VaDE suffers from two problems: 1) it is fragile to the input noise; 2) it ignores the locality information between the neighboring data points. In this paper, we propose a joint learning framework that improves VaDE with a robust embedding discriminator and a local structure constraint, which are both helpful to improve the robustness of our model. Experiment results on various vision and textual datasets demonstrate that our method outperforms the state-of-the-art baseline models in all metrics. Further detailed analysis shows that our proposed model is very robust to the adversarial inputs, which is a desirable property for practical applications.

本文提出了一种改进的联合学习框架，包括强鲁棒性嵌入判别器和局部结构约束，来改善VaDE聚类方法的两个问题。实验结果表明，我们的方法在各种基准数据集上表现优于现有模型，并且对抗输入具有极强的稳健性，可实际应用于实践。

通过保持局部性的变分判别网络学习强大的聚类表示