  The unsupervised outlier detection (UOD) problem refers to a task to identify
inliers given training data which contain outliers as well as inliers, without
any labeled information about inliers and outliers. It has been widely
recognized that using fully-trained likelihood-based deep generative models
(DGMs) often results in poor performance in distinguishing inliers from
outliers. In this study, we claim that the likelihood itself could serve as
powerful evidence for identifying inliers in UOD tasks, provided that DGMs are
carefully under-fitted. Our approach begins with a novel observation called the
inlier-memorization (IM) effect-when training a deep generative model with data
including outliers, the model initially memorizes inliers before outliers.
Based on this finding, we develop a new method called the outlier detection via
the IM effect (ODIM). Remarkably, the ODIM requires only a few updates, making
it computationally efficient-at least tens of times faster than other
deep-learning-based algorithms. Also, the ODIM filters out outliers
excellently, regardless of the data type, including tabular, image, and text
data. To validate the superiority and efficiency of our method, we provide
extensive empirical analyses on close to 60 datasets.
