  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.
