Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data, thus usually only few known anomalies if any are available. Inspired by generative models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called DA3D. Here, we use adversarial autoencoders to generate anomalous counterexamples based on the normal data only. These artificial anomalies used during training allow the detection of real, yet unseen anomalies. With our novel generative approach, we transform the unsupervised task of anomaly detection to a supervised one, which is more tractable by machine learning and especially deep learning methods. DA3D surpasses the performance of state-of-the-art anomaly detection methods in a purely data-driven way, where no domain knowledge is required.

翻译：异常检测对机器学习算法而言是一项具有挑战性的任务，其根源在于固有的类别不平衡问题。手动分析观测数据成本高昂且耗时，因此通常只能获取少量已知异常样本（若有的话）。受生成模型及神经网络隐藏激活分析的启发，我们提出一种名为DA3D的新型无监督异常检测方法。该方法利用对抗自编码器仅基于正常数据生成异常反例。训练阶段使用这些人工生成的异常样本，使得模型能够检测到真实但未被观测到的异常。通过这一新颖的生成式方法，我们将无监督异常检测任务转化为监督学习问题，后者更易于机器学习（尤其是深度学习方法）处理。DA3D以纯数据驱动的方式（无需领域知识）超越了现有最优异常检测方法的性能。