2019/05/26 by Alexander Tong, Guy Wolf, Tong, Alexander +3 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Bacillus and Francisella bacterial research #Computer Vision and Pattern Recognition (cs.CV) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.10710
6 pages, 4 figures, 2 tables, presented at IEEE MLSP
openalex publication_date 2019/05/26 · arxiv published 2019/05/26 · openalex created_date 2020/02/14 · arxiv created 2020/07/26 · arxiv updated 2020/07/28 · openalex updated_date 2026/07/28
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other errors. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods are sensitive to training-data outliers and simple-to-reconstruct points. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator that does not suffer from these biases. Our anomaly discriminator is trained, similar to the ones used in GANs, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data.