  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.
