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 2651–2700 of 4856 papers

TitleStatusHype
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM—0
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly DetectionCode1
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw PuzzlesCode1
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle—0
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images—0
Informative knowledge distillation for image anomaly segmentationCode1
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation—0
Task-aware Similarity Learning for Event-triggered Time Series—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
Registration based Few-Shot Anomaly DetectionCode2
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations—0
A Benchmark dataset for predictive maintenance—0
Stochastic Functional Analysis and Multilevel Vector Field Anomaly Detection—0
On the Robustness and Anomaly Detection of Sparse Neural Networks—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
Generative Adversarial Networks and Other Generative Models—0
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Deep Learning for Anomaly Detection in Log Data: A SurveyCode1
ENCODE: Encoding NetFlows for Network Anomaly DetectionCode0
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors—0
Leveraging Log Instructions in Log-based Anomaly DetectionCode0
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set RecognitionCode1
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly DetectionCode0
Leveraging Trajectory Prediction for Pedestrian Video Anomaly DetectionCode1
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Task-oriented Self-supervised Learning for Anomaly Detection in ElectroencephalographyCode1
Augment to Detect Anomalies with Continuous Labelling—0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space—0
Multivariate Time Series Anomaly Detection with Few Positive SamplesCode1
A geometric framework for outlier detection in high-dimensional data—0
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection—0
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis—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