  Adversarial examples causing evasive predictions are widely used to evaluate
and improve the robustness of machine learning models. However, current studies
focus on supervised learning tasks, relying on the ground-truth data label, a
targeted objective, or supervision from a trained classifier. In this paper, we
propose a framework of generating adversarial examples for unsupervised models
and demonstrate novel applications to data augmentation. Our framework exploits
a mutual information neural estimator as an information-theoretic similarity
measure to generate adversarial examples without supervision. We propose a new
MinMax algorithm with provable convergence guarantees for efficient generation
of unsupervised adversarial examples. Our framework can also be extended to
supervised adversarial examples. When using unsupervised adversarial examples
as a simple plug-in data augmentation tool for model retraining, significant
improvements are consistently observed across different unsupervised tasks and
datasets, including data reconstruction, representation learning, and
contrastive learning. Our results show novel methods and considerable
advantages in studying and improving unsupervised machine learning via
adversarial examples.
