arXiv Machine Learning
Jun 16
Anomaly Detection via Mean Shift Density Enhancement
arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
arXiv:2606. 18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.
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arXiv AI
Jun 2
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
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.
arXiv Machine Learning
Jun 18
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
arXiv:2606. 19255v1 Announce Type: new Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications.
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.
arXiv Machine Learning
Jun 17
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
arXiv:2505. 03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision.
arXiv AI
Aug 10
FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition
arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).
arXiv AI
Jul 28
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
arXiv Machine Learning
Jul 31
ARES: Anomaly Recognition Model For Edge Streams
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv AI
Jun 30
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
arXiv:2606. 28970v1 Announce Type: cross Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient.
Hugging Face Trending Papers
Jul 27
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.
arXiv Machine Learning
Jun 15
D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection
arXiv:2606. 13754v1 Announce Type: new Abstract: Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments.
arXiv Machine Learning
Aug 6
Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection
arXiv:2608. 04753v1 Announce Type: new Abstract: Attention layers are the backbone of today's most powerful and impactful models.