arXiv AI
Sep 25

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

The paper introduces a deep positive‑unlabeled anomaly detection framework that combines positive‑unlabeled learning with deep models such as autoencoders and deep support vector data descriptions. It addresses the issue of contaminated unlabeled data by approximating anomaly scores for normal data using both unlabeled and labeled anomaly samples, allowing training without labeled normal data. The authors provide a theoretical generalization error bound and demonstrate improved detection performance over existing methods on several datasets.

By Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka
arXiv Machine Learning
Jun 19

We Need to Rethink Benchmarking in Anomaly Detection

arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.

By Philipp R\"ochner, Simon Kl\"uttermann, Kevin Kammler, Franz Rothlauf, Emmanuel M\"uller, Daniel Schl\"or
arXiv Machine Learning
Sep 23

Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?

The paper investigates whether the performance of anomaly detection systems can be predicted without labeled anomalies. For kNN-based detectors, it derives a lower bound on AUC that links detection performance to the separation and variance of inlier and outlier scores, and uses this to analyze how density variation, intrinsic dimensionality, and domain mismatch affect score variability. The authors introduce pseudo‑anomaly probes that provide a reference for estimating relative score separation, and demonstrate through experiments on DCASE benchmarks that these probes enable anomaly‑free model selection to outperform conventional development‑set selection, especially under domain shift.

By Kevin Wilkinghoff, Zheng-Hua Tan