arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
By Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej
The paper investigates why reconstruction-based unsupervised learning can fail, identifying two failure modes: over‑reconstruction of anomalies and loss of nominal variation. Using the Pursuit of Subspaces hypothesis, it links these failures to geometric properties—join blindness from excess range and meet preference from insufficient capacity—and shows that a compact nominal union is optimal, typically requiring a nonlinear reconstruction map. The authors propose Dynamic Push and Pull, along with nested manifold carving, to learn compact representations without anomaly labels, and demonstrate improved anomaly detection on standard benchmarks, unseen image degradations, and ECG classification.
By Mehmet Yama\c{c}, Yagmur Mustu, Muhammad Numan Yousaf, Lei Xu, Marcel van Gerven
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
arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie
arXiv:2606. 04073v1 Announce Type: cross Abstract: This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\textbf{T}wo-stage \textbf{P}seudo \textbf{A}nomaly-guided \textbf{A}nomaly \textbf{D}etection, \textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training.
By Xiancheng Wang, Zhibo Zhang, Ran Li, Rui Wang, Minghang Zhao, Shisheng Zhong, Lin Wang
The paper introduces a variational template matching framework for anomaly detection in patterned structures, representing anomaly templates as transformed instances and using normalized cross‑correlation across the transformation space. It enhances robustness by adding a density‑based statistical anomaly score derived from local intensity distributions via kernel density estimation, which captures distributional concentration and tail behavior more effectively than histogram methods. The structural and statistical cues are fused in a unified formulation, and experiments on biological cell images show the method outperforms classical baselines and rivals ResNet‑50 while remaining fully training‑free and providing explicit localization.
By Qinwu Xu, Yifan Jiang