Title: Learning to Generate Pseudo Anomalies. Abstract: Due to rare occurrence of anomalous events, anomaly detection is often seen as one-class classification (OCC) problem. In this setting, an autoencoder (AE) is typically trained to reconstruct using only normal data in order to learn normalcy representations. It is expected that, at test time, the AE can well reconstruct normal data while poorly reconstructing anomalous data. However, anomalous data is often well reconstructed as well. This phenomenon can be attributed to the fact that when training AE with only normal data, the boundary between normal and abnormal data is unknown, consequently resulting in a boundary that includes the abnormal data as well. To alleviate this problem, we utilize pseudo anomalies to limit the reconstruction capability of an AE. Without imposing strong inductive bias, pseudo anomalies are generated by adding noise to the normal data. Moreover, to improve the quality of pseudo anomalies, we propose a learning mechanism to generate noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. Evaluations on Ped2, Avenue, ShanghaiTech, and CIFAR-10 datasets demonstrate the effectiveness of our approach in improving the discriminative capability of AEs for anomaly detection.