Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Neural Netw. 2021 Dec;144:726-736. doi: 10.1016/j.neunet.2021.09.014. Epub 2021 Sep 28.
Autoencoders 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 failing to regenerate the anomalous data. Based on this assumption, one could utilize the AE for novelty detection. However, it is known that this assumption does not always hold. Such an AE can often perfectly reconstruct the anomalous data due to modeling low-level and generic features in the input. We propose a novel training algorithm for the AE that facilitates learning more semantically meaningful features to address this problem. For this purpose, we exploit the fact that adversarial robustness promotes the learning of significant features. Therefore, we force the AE to learn such features by making its bottleneck layer more stable against adversarial perturbations. This idea is general and can be applied to other autoencoder-based approaches as well. We show that despite using a much simpler architecture than the prior methods, the proposed AE outperforms or is competitive to the state-of-the-art on four benchmark datasets and two medical datasets.
自动编码器最近被广泛应用于解决新颖性检测问题。仅在正常数据上进行训练，AE 有望有效地重建正常数据，而无法再生异常数据。基于这个假设，人们可以利用 AE 进行新颖性检测。然而，众所周知，这种假设并不总是成立。由于对输入中的低级和通用特征进行建模，这种 AE 通常可以完美地重建异常数据。我们提出了一种新颖的 AE 训练算法，以解决这个问题，促进学习更具语义意义的特征。为此，我们利用对抗鲁棒性促进重要特征学习的事实。因此，我们通过使 AE 的瓶颈层对对抗扰动更稳定来迫使 AE 学习这些特征。这个想法是通用的，也可以应用于其他基于自动编码器的方法。我们表明，尽管所提出的 AE 使用的架构比先前的方法简单得多，但它在四个基准数据集和两个医学数据集上的表现优于或与最先进的方法相当。
