  Unsupervised anomaly detection aims to build models to effectively detect
unseen anomalies by only training on the normal data. Although previous
reconstruction-based methods have made fruitful progress, their generalization
ability is limited due to two critical challenges. First, the training dataset
only contains normal patterns, which limits the model generalization ability.
Second, the feature representations learned by existing models often lack
representativeness which hampers the ability to preserve the diversity of
normal patterns. In this paper, we propose a novel approach called Adaptive
Memory Network with Self-supervised Learning (AMSL) to address these challenges
and enhance the generalization ability in unsupervised anomaly detection. Based
on the convolutional autoencoder structure, AMSL incorporates a self-supervised
learning module to learn general normal patterns and an adaptive memory fusion
module to learn rich feature representations. Experiments on four public
multivariate time series datasets demonstrate that AMSL significantly improves
the performance compared to other state-of-the-art methods. Specifically, on
the largest CAP sleep stage detection dataset with 900 million samples, AMSL
outperforms the second-best baseline by \textbf{4}\%+ in both accuracy and F1
score. Apart from the enhanced generalization ability, AMSL is also more robust
against input noise.
