  Anomaly detection and localization without any manual annotations and prior
knowledge is a challenging task under the setting of unsupervised learning. The
existing works achieve excellent performance in the anomaly detection, but with
complex networks or cumbersome pipelines. To address this issue, this paper
explores a simple but effective architecture in the anomaly detection. It
consists of a well pre-trained encoder to extract hierarchical feature
representations and a decoder to reconstruct these intermediate features from
the encoder. In particular, it does not require any data augmentations and
anomalous images for training. The anomalies can be detected when the decoder
fails to reconstruct features well, and then errors of hierarchical feature
reconstruction are aggregated into an anomaly map to achieve anomaly
localization. The difference comparison between those features of encoder and
decode lead to more accurate and robust localization results than the
comparison in single feature or pixel-by-pixel comparison in the conventional
works. Experiment results show that the proposed method outperforms the
state-of-the-art methods on MNIST, Fashion-MNIST, CIFAR-10, and MVTec Anomaly
Detection datasets on both anomaly detection and localization.
