Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data is often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection.

翻译：由于异常事件很少发生，一种典型的异常检测方法仅使用正常数据训练自编码器（AE），使其学习正常训练数据的模式或特征表示。在测试阶段，训练好的自编码器应能良好重建正常数据，但对异常数据的重建效果较差。然而，与预期相反，异常数据往往也能得到较好的重建。为了进一步分离正常数据与异常数据的重建质量，我们提出通过利用上述自编码器的弱点（即对异常数据过度重建）来从学习的自适应噪声中生成伪异常。生成的噪声被添加到正常数据中以创建伪异常。在Ped2、Avenue、ShanghaiTech、CIFAR-10和KDDCUP数据集上的大量实验表明，我们的方法在提升自编码器异常检测判别能力方面具有有效性和通用适用性。