  Most deep anomaly detection models are based on learning normality from
datasets due to the difficulty of defining abnormality by its diverse and
inconsistent nature. Therefore, it has been a common practice to learn
normality under the assumption that anomalous data are absent in a training
dataset, which we call normality assumption. However, in practice, the
normality assumption is often violated due to the nature of real data
distributions that includes anomalous tails, i.e., a contaminated dataset.
Thereby, the gap between the assumption and actual training data affects
detrimentally in learning of an anomaly detection model. In this work, we
propose a learning framework to reduce this gap and achieve better normality
representation. Our key idea is to identify sample-wise normality and utilize
it as an importance weight, which is updated iteratively during the training.
Our framework is designed to be model-agnostic and hyperparameter insensitive
so that it applies to a wide range of existing methods without careful
parameter tuning. We apply our framework to three different representative
approaches of deep anomaly detection that are classified into one-class
classification-, probabilistic model-, and reconstruction-based approaches. In
addition, we address the importance of a termination condition for iterative
methods and propose a termination criterion inspired by the anomaly detection
objective. We validate that our framework improves the robustness of the
anomaly detection models under different levels of contamination ratios on five
anomaly detection benchmark datasets and two image datasets. On various
contaminated datasets, our framework improves the performance of three
representative anomaly detection methods, measured by area under the ROC curve.
