Incorporating Decision-level Reconstruction Quality in Adversarial Autoencoders for Anomaly Detection
Luyuan Li, Andy Jinhua Ma · 2021
Anomaly detection, also known as one-class classification, is a challenging problem due to the absence of abnormal data for training. One of the promising approaches for image anomaly detection is to employ adversarial autoencoders for anomaly measurement by computing the pixel-level reconstruction errors. However, such pixel-level anomaly measure is very sensitive to individual pixels with huge reconstruction difference, resulting in large errors even on normal images. In this paper, we propose a novel anomaly detection method by combining decision-level reconstruction quality with pixel-level reconstruction error. In the proposed method, an adversarial autoencoder is trained to not only measure pixel-level reconstruction error, but also select typical and atypical normal samples from the train set. These selected samples are used to generate positive samples with good reconstruction quality and pseudo negative samples with poor quality respectively for training a classifier which measures the decision-level reconstruction quality of input samples. Experiments on public benchmark datasets show that our method achieves better results by incorporating decision-level reconstruction quality with pixel-level reconstruction error for anomaly detection, and outperforms a wide range of existing methods.