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

Showing 26512675 of 4856 papers

TitleStatusHype
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series0
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 EGM0
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 Series0
A general-purpose method for applying Explainable AI for Anomaly Detection0
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 Network0
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw PuzzlesCode1
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks0
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle0
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images0
Informative knowledge distillation for image anomaly segmentationCode1
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation0
Task-aware Similarity Learning for Event-triggered Time Series0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection0
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 Transfers0
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations0
A Benchmark dataset for predictive maintenance0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified