  We present a simple framework for one-class classification and anomaly
detection. The core idea is to learn a mapping to transform the unknown
distribution of training (normal) data to a known target distribution.
Crucially, the target distribution should be sufficiently simple, compact, and
informative. The simplicity is to ensure that we can sample from the
distribution easily, the compactness is to ensure that the decision boundary
between normal data and abnormal data is clear and reliable, and the
informativeness is to ensure that the transformed data preserve the important
information of the original data. Therefore, we propose to use truncated
Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between
hyperspheres, as the target distribution. We then minimize the distance between
the transformed data distribution and the target distribution while keeping the
reconstruction error for the original data small enough. Comparative studies on
multiple benchmark datasets verify the effectiveness of our methods in
comparison to baselines.
