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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 2901–2950 of 4856 papers

TitleStatusHype
Surface abnormality detection in medical and inspection systems using energy variations in co-occurrence matrixes—0
Multi-Task Learning based Video Anomaly Detection with Attention—0
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
A Survey on Explainable Anomaly Detection—0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
Anomaly detection in dynamic networksCode0
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans—0
Anomaly Detection via Federated Learning—0
Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels—0
InQMAD: Incremental Quantum Measurement Anomaly DetectionCode0
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian LearningCode0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Anomaly detection using data depth: multivariate case—0
Null Hypothesis Test for Anomaly DetectionCode0
Improved Anomaly Detection by Using the Attention-Based Isolation ForestCode0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive LearningCode0
Detecting Irregular Network Activity with Adversarial Learning and Expert FeedbackCode0
A Lightweight Yet Robust Approach to Textual Anomaly Detection—0
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional AutoencodersCode0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Spatio-Temporal Relation Learning for Video Anomaly Detection—0
Real-time Anomaly Detection for Multivariate Data Streams—0
Activation Learning by Local Competitions—0
Enhancing Claim Classification with Feature Extraction from Anomaly-Detection-Derived Routine and Peculiarity Profiles—0
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection—0
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation—0
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point CloudsCode0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
Oracle Analysis of Representations for Deep Open Set Detection—0
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping—0
Self-supervised Learning for Clustering of Wireless Spectrum ActivityCode0
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes—0
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics—0
Learning Acceptance Regions for Many Classes with Anomaly Detection—0
Collaborative Anomaly Detection—0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model—0
Infrared: A Meta Bug Detector—0
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks—0
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning—0
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning—0
Analytics and Machine Learning Powered Wireless Network Optimization and Planning—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
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (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