  One-class classification has been a prevailing method in building deep
anomaly detection models under the assumption that a dataset consisting of
normal samples is available. In practice, however, abnormal samples are often
mixed in a training dataset, and they detrimentally affect the training of deep
models, which limits their applicability. For robust normality learning of deep
practical models, we propose an unsupervised deep one-class classification that
learns normality from pseudo-labeled normal samples, i.e., outlier detection in
single cluster scenarios. To this end, we propose a pseudo-labeling method by
an adaptive threshold selected by ranking-based training dynamics. The
experiments on 10 anomaly detection benchmarks show that our method effectively
improves performance on anomaly detection by sizable margins.
