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.
翻译:导致规避预测的相反例子被广泛用于评价和改善机器学习模型的稳健性,然而,目前研究的重点是监督的学习任务,依靠地面实况数据标签、目标目标或受过训练的分类员的监督。在本文件中,我们提出了一个框架,为不受监督的模型生成对抗性实例,并展示数据扩增的新应用。我们的框架利用一个相互的信息神经测算器作为信息理论相似性措施,在没有监督的情况下生成对抗性实例。我们提出了一个新的 MinMax 算法,为有效生成不受监督的对抗性实例提供可辨识的趋同保证。我们的框架还可以扩展至受监督的对抗性实例。在使用未经监督的对抗性实例作为模型再培训的简单插入数据增强工具时,在不同的不受监督的任务和数据集中,包括数据重建、代表性学习和对比性学习,不断观察到重大改进。我们的成果显示了通过对抗性实例研究和改进未经监督的机器学习的新方法和相当大的优势。