Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle under small adversarial perturbations. In this paper, we propose a simple and effective method, Adversarial Training with informative Outlier Mining (ATOM), to robustify OOD detection. Our key observation is that while unlabeled data can be used as auxiliary OOD training data, the majority of these data points are not informative to improve the decision boundary of the OOD detector. We show that, by carefully choosing which outliers to train on, one can significantly improve the robustness of the OOD detector, and somewhat surprisingly, generalize to some adversarial attacks not seen during training. We provide additionally a unified evaluation framework that allows future research examining the robustness of OOD detection algorithms. ATOM achieves state-of-the-art performance under a broad family of natural and perturbed OOD evaluation tasks, surpassing previous methods by a large margin. Finally, we provide theoretical insights for the benefit of auxiliary unlabeled data and outlier mining.

本研究提出了一种理论上动机驱动、运用了有意义外部数据挖掘的对抗训练算法 ATOM，从而提高了深度学习模型的Out-of-Distribution Detection能力，在包括对抗式输入在内的广泛经典OOD检测任务中，实现了最佳性能。

ATOM: 使用异常值挖掘强化外部数据分布检测