  Outlier detection is one of the most important processes taken to create
good, reliable data in machine learning. The most methods of outlier detection
leverage an auxiliary reconstruction task by assuming that outliers are more
difficult to be recovered than normal samples (inliers). However, it is not
always true, especially for auto-encoder (AE) based models. They may recover
certain outliers even outliers are not in the training data, because they do
not constrain the feature learning. Instead, we think outlier detection can be
done in the feature space by measuring the feature distance between outliers
and inliers. We then propose a framework, MCOD, using a memory module and a
contrastive learning module. The memory module constrains the consistency of
features, which represent the normal data. The contrastive learning module
learns more discriminating features, which boosts the distinction between
outliers and inliers. Extensive experiments on four benchmark datasets show
that our proposed MCOD achieves a considerable performance and outperforms nine
state-of-the-art methods.
