Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning

TL;DR

This paper introduces a novel framework for generating adversarial examples in unsupervised machine learning, using an information-theoretic approach, which significantly enhances model robustness and performance across various tasks.

Contribution

It presents a new unsupervised adversarial example generation method with a provably convergent MinMax algorithm, extending adversarial techniques beyond supervised learning.

Findings

Significant performance improvements in unsupervised tasks

Effective data augmentation for model retraining

Versatile application across multiple datasets

Abstract

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.