Supplementary Material for Anomaly Detection Benchmark

We implement several representative supervised classification algorithms in ADBench (as shown in Appx. B.1), and recommend interesting readers to recent machine learning books [ To this end, some recent studies investigate efficiently using partially labeled data for improving detection performance, and leverage the unlabeled data to facilitate representation learning. As we show in Table 1, there is a line of existing AD benchmarks. A GAN-based method that defines the reconstruction error of the input instance as the anomaly score. The hidden size of REPEN is set to 20, and the margin of triplet loss is set to 1000.