论文标题
ARAE：自动编码器的对抗性训练可改善新颖性检测
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
论文作者
论文摘要
自动编码器（AE）最近被广泛用于解决新颖性检测问题。 AE仅根据正常数据进行培训，而AE有望有效地重建正常数据，而未能再生异常数据，这些数据可用于新颖性检测。但是，在本文中，这证明这并不总是存在。 AE通常会非常完美地概括，以至于它也可以很好地重建异常数据。为了解决这个问题，我们提出了一个小说AE，可以学习更有意义的特征。具体而言，我们利用了一个事实，即对抗性鲁棒性促进了有意义的特征的学习。因此，我们迫使AE通过用瓶颈层对网络进行惩罚，而瓶颈层不稳定，这是不稳定的。我们表明，尽管与先前方法相比，使用了更简单的体系结构，但所提出的AE胜过表现，或者在三个基准数据集中与最先进的架构竞争。
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.
