Autoencoders (AE) have recently been widely employed to approach the novelty
detection problem. Trained only on the normal data, the AE is expected to
reconstruct the normal data effectively while fail to regenerate the anomalous
data, which could be utilized for novelty detection. However, in this paper, it
is demonstrated that this does not always hold. AE often generalizes so
perfectly that it can also reconstruct the anomalous data well. To address this
problem, we propose a novel AE that can learn more semantically meaningful
features. Specifically, we exploit the fact that adversarial robustness
promotes learning of meaningful features. Therefore, we force the AE to learn
such features by penalizing networks with a bottleneck layer that is unstable
against adversarial perturbations. We show that despite using a much simpler
architecture in comparison to the prior methods, the proposed AE outperforms or
is competitive to state-of-the-art on three benchmark datasets.

本文提出了一种新型自编码器，利用对抗鲁棒性来学习更具语义意义的特征以在新颖性检测问题中更好地重建正常数据，并提出奖励惩罚机制以解决自编码器过于泛化的问题，实验证明该方法在三个基准数据集上超越或与最先进的方法相竞争。