DIFFINT is a reconstruction‑based anomaly detector that uses a differentiable autoencoder with a latent bottleneck composed of soft, axis‑aligned interval memberships. Each latent unit represents a human‑readable hyper‑rectangle in feature space, allowing the model to encode how strongly an instance falls inside each interval and to compute reconstruction error as the anomaly score. The method provides a certified lower bound on reconstruction error for points outside all active intervals, a suppression mechanism for sparse abnormalities, and a closed‑form, label‑free importance ranking for each (unit, feature) pair, achieving top performance on 48 ADBench benchmarks against 22 baselines.
By Lamine Diop, Marc Plantevit
WAND is an unsupervised tabular anomaly detector that scores each point by how far its projection on unit‑sphere directions deviates from a sub‑Gaussian baseline. The directions that flag a point serve as its explanation, providing per‑feature attribution at no extra cost and recoverable via gradients. On 47 ADBench datasets, WAND matches or exceeds 16 baselines in ROC‑AUC while delivering more accurate, faithful explanations than post‑hoc SHAP, LIME, or ECOD, all with linear scoring time and a probe‑efficiency guarantee.
By Lamine Diop
The paper studies a class of tabular anomaly detectors that train a neural map toward a fixed target using squared‑error loss, noting that improved optimization can paradoxically worsen detection performance—a phenomenon termed convergence collapse. It demonstrates that this collapse arises because the learned map extrapolates beyond the training data, erasing residual signals on anomalies. To address this, the authors introduce Kernel Contraction Matching (KCM), a closed‑form, training‑free kernel‑based baseline, and propose the Kernel‑Anchored Regularizer (KAR), which penalizes deviations from a kernel‑weighted average of training targets, thereby mitigating collapse and improving AUROC during extended training.
By Jos\'e Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Li\^en Doan
arXiv:2610.09670v1 Announce Type: new
Abstract: Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstr...
By Shouhei Hanaoka, Takahiro Nakao, Atsushi Takamatsu, Takeharu Yoshikawa, Osamu Abe
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
LEAP is a new time‑series anomaly detection model that avoids reconstruction by using masked prediction and latent‑embedding evidence. It removes patches from a window, predicts them from remaining context, and scores prediction errors as anomaly evidence. The model incorporates three label‑free novelty signals—latent‑embedding novelty, local temporal contrast, and per‑channel novelty—and achieves strong performance on the TSB‑AD benchmark, with statistically significant gains from each component.
By Cristhian Moya-Mota, Ignacio Aguilera-Martos, Diego Garc\'ia-Gil, Juli\'an Luengo