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 3626–3650 of 4856 papers

TitleStatusHype
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks—0
Global Information Guided Video Anomaly Detection—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance—0
Using a Neural Network to Detect Anomalies given an N-gram Profile—0
LearningCity: Knowledge Generation for Smart Cities—0
Efficient Model Monitoring for Quality Control in Cardiac Image SegmentationCode0
Smart Vectorizations for Single and Multiparameter PersistenceCode0
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps—0
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoTCode0
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite RankingCode0
Concentration bounds for the empirical angular measure with statistical learning applicationsCode0
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses—0
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection—0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection—0
Isconna: Streaming Anomaly Detection with Frequency and PatternsCode0
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection—0
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation—0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Anomaly Detection Under Multiplicative Noise Model Uncertainty—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