  Image reconstruction-based anomaly detection has recently been in the
spotlight because of the difficulty of constructing anomaly datasets. These
approaches work by learning to model normal features without seeing abnormal
samples during training and then discriminating anomalies at test time based on
the reconstructive errors. However, these models have limitations in
reconstructing the abnormal samples due to their indiscriminate conveyance of
features. Moreover, these approaches are not explicitly optimized for
distinguishable anomalies. To address these problems, we propose a two-stream
decoder network (TSDN), designed to learn both normal and abnormal features.
Additionally, we propose a feature normality estimator (FNE) to eliminate
abnormal features and prevent high-quality reconstruction of abnormal regions.
Evaluation on a standard benchmark demonstrated performance better than
state-of-the-art models.
