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 2501–2550 of 4856 papers

TitleStatusHype
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
N-pad : Neighboring Pixel-based Industrial Anomaly Detection—0
Multi-Task Learning based Video Anomaly Detection with Attention—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionCode1
Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentCode1
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection—0
Surface abnormality detection in medical and inspection systems using energy variations in co-occurrence matrixes—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Anomaly detection in dynamic networksCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
A Survey on Explainable Anomaly Detection—0
Overlooked Video Classification in Weakly Supervised Video Anomaly DetectionCode1
Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels—0
Anomaly Detection via Federated Learning—0
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans—0
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian LearningCode0
InQMAD: Incremental Quantum Measurement Anomaly DetectionCode0
Towards Continual Adaptation in Industrial Anomaly DetectionCode1
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and LocalizationCode1
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagesCode1
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
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
TimesNet: Temporal 2D-Variation Modeling for General Time Series AnalysisCode6
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive LearningCode0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
A Lightweight Yet Robust Approach to Textual Anomaly Detection—0
Detecting Irregular Network Activity with Adversarial Learning and Expert FeedbackCode0
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional AutoencodersCode0
Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—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
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation—0
Enhancing Claim Classification with Feature Extraction from Anomaly-Detection-Derived Routine and Peculiarity Profiles—0
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine LearningCode1
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection—0
Activation Learning by Local Competitions—0
Real-time Anomaly Detection for Multivariate Data Streams—0
Anomaly Detection in Aerial Videos with TransformersCode1
Self-Supervised Masked Convolutional Transformer Block for Anomaly DetectionCode1
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point CloudsCode0
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection ModelCode1
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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