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 1601–1625 of 4856 papers

TitleStatusHype
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices—0
Detecting Log Anomalies with Multi-Head Attention (LAMA)—0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
Detecting Novelties with Empty Classes—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models—0
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
A Review of Machine Learning based Anomaly Detection Techniques—0
Detecting Relative Anomaly—0
Anomize: Better Open Vocabulary Video Anomaly Detection—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—0
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach—0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning—0
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code—0
Detection and Statistical Modeling of Birth-Death Anomaly—0
AAD: Adaptive Anomaly Detection through traffic surveillance videos—0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )—0
Detection of Abnormal Behavior with Self-Supervised Gaze Estimation—0
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean—0
Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model—0
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System—0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—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