Abstract: A computer-implemented method and system for training an anomaly detector to distinguish outlier data from inlier data on which the anomaly detector is trained. The anomaly detector comprises a set of learnable data transformations and a learnable feature extractor. The set of learnable data transformations and the learnable feature extractor are jointly trained based on a trained objective, which training objective comprises a function serving as anomaly scoring function which may also be used at test time to determine the anomaly score of test data samples. Evaluation results show that the anomaly detector is well-applicable to detect anomalies in non-image data, e.g., in data timeseries and in tabular data, and straightforward to apply at test time.