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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 551–600 of 4856 papers

TitleStatusHype
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
Camouflaged Object DetectionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease PredictionCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
CHAD: Charlotte Anomaly DatasetCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Class Label-aware Graph Anomaly DetectionCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly DetectionCode1
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedInCode1
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing FlowCode1
Can LLMs Understand Time Series Anomalies?Code1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation LearningCode1
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Anomaly detection in surveillance videos using transformer based attention modelCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and ReasoningCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
DAGAD: Data Augmentation for Graph Anomaly DetectionCode1
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Deep Anomaly Detection on Attributed NetworksCode1
Deep Anomaly Detection Using Geometric TransformationsCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Anomaly Detection in Video Sequences: A Benchmark and Computational ModelCode1
Alleviating Structural Distribution Shift in Graph Anomaly DetectionCode1
Deep Generative Classification of Blood Cell MorphologyCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
Deep Isolation Forest for Anomaly DetectionCode1
Deep Learning for Anomaly Detection in Log Data: A SurveyCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
Anomaly Detection under Distribution ShiftCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
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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