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 4651–4675 of 4856 papers

TitleStatusHype
Brain Abnormality Detection by Deep Convolutional Neural Network—0
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning—0
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity—0
Anomaly Detection with Robust Deep AutoencodersCode0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Anomaly Detection on Graph Time Series—0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Anomaly Detection by Robust Statistics—0
leave a trace - A People Tracking System Meets Anomaly Detection—0
Model Selection for Anomaly Detection—0
Anomaly Detection and Modeling in 802.11 Wireless Networks—0
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification—0
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds—0
Concept Drift and Anomaly Detection in Graph StreamsCode0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups—0
Interaction-Based Distributed Learning in Cyber-Physical and Social Networks—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Time Series Data Cleaning: From Anomaly Detection to Anomaly RepairingCode0
Unsupervised real-time anomaly detection for streaming dataCode0
Recurrent Estimation of Distributions—0
Temporal anomaly detection: calibrating the surpriseCode0
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks—0
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata—0
Unmasking the abnormal events in video—0
Online learnability of Statistical Relational Learning in anomaly detection—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