Autoencoder (AE) is a neural network (NN) architecture that is trained to reconstruct an input at its output. By measuring the reconstruction errors of new input samples, AE can detect anomalous samples deviated from the trained data distribution. The key to success is to achieve high-fidelity reconstruction (HFR) while restricting AE's capability of generalization beyond training data, which should be balanced commonly via iterative re-training. Alternatively, we propose a novel framework of AE-based anomaly detection, coined HFR-AE, by projecting new inputs into a subspace wherein the trained AE achieves HFR, thereby increasing the gap between normal and anomalous sample reconstruction errors. Simulation results corroborate that HFR-AE improves the area under receiver operating characteristic curve (AUROC) under different AE architectures and settings by up to 13.4% compared to Vanilla AE-based anomaly detection.

翻译：自动编码器(AE)是一个神经网络(NN)结构,经过培训可以重建输出输入。通过测量新输入样本的重建错误,AE可以检测出偏离经过培训的数据分布的异常样本。成功的关键是实现高度忠诚重建(HFR),同时将AE的概括能力限制在培训数据之外,而培训数据通常应通过迭代再培训加以平衡。或者,我们提议一个基于AE的异常现象探测新框架,由HFR-AE共同创建,将新输入投射到一个子空间,由受过培训的AE实现HFR, 从而扩大正常与异常样本重建错误之间的差距。模拟结果证实,HFR-AE将不同AE结构下接收器运行特征曲线的面积改进到13.4%,而Vanilla AE的异常现象检测则为13.4%。