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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1376–1400 of 4856 papers

TitleStatusHype
Signature Isolation Forest—0
Dual-path Frequency Discriminators for Few-shot Anomaly DetectionCode1
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks—0
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural NetworksCode0
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1—0
Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots—0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation—0
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series—0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
CSE: Surface Anomaly Detection with Contrastively Selected EmbeddingCode0
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global FeaturesCode2
Learn Suspected Anomalies from Event Prompts for Video Anomaly DetectionCode0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks—0
Dimensionality reduction techniques to support insider trading detection—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
UniTS: A Unified Multi-Task Time Series ModelCode4
A SAM-guided Two-stream Lightweight Model for Anomaly DetectionCode1
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online AdaptationCode2
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection—0
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling—0
Continuous Memory Representation for Anomaly DetectionCode1
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model—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