SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 76–100 of 4856 papers

TitleStatusHype
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach—0
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-TuningCode2
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms—0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Is Hyperbolic Space All You Need for Medical Anomaly Detection?—0
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning—0
Fog Intelligence for Network Anomaly Detection—0
RoBiS: Robust Binary Segmentation for High-Resolution Industrial ImagesCode1
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
Cellwise and Casewise Robust Covariance in High Dimensions—0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—0
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-ThoughtCode1
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
Rethinking Metrics and Benchmarks of Video Anomaly Detection—0
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly DetectionCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Learning Normal Patterns in Musical Loops—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified