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 651–675 of 4856 papers

TitleStatusHype
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
Unsupervised Event Outlier Detection in Continuous Time—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
Circuit design in biology and machine learning. II. Anomaly detection—0
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters—0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation—0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial ManufacturingCode0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—0
Privacy-Preserving Video Anomaly Detection: A Survey—0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection—0
Is this Generated Person Existed in Real-world? Fine-grained Detecting and Calibrating Abnormal Human-body—0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Quantized symbolic time series approximationCode2
Demonstrating the Suitability of Neuromorphic, Event-Based, Dynamic Vision Sensors for In Process Monitoring of Metallic Additive Manufacturing and Welding—0
Associative Knowledge Graphs for Efficient Sequence Storage and RetrievalCode0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
AI Guided Early Screening of Cervical Cancer—0
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly DetectionCode1
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings—0
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection—0
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance—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