Title: Restricted Generative Projection for One-Class Classification and Anomaly detection. Abstract: We present a novel 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 distribution that is supposed to be different from the transformed distribution of unknown abnormal data. Crucially, the target distribution of training data 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 hyperball, 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. Our model is simple and easy to train especially compared with those based on generative models. Comparative studies on a few benchmark datasets verify the effectiveness of our method in comparison to baselines.