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 126–150 of 4856 papers

TitleStatusHype
Fairness-aware Anomaly Detection via Fair Projection—0
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark—0
Preference Isolation Forest for Structure-based Anomaly Detection—0
Hashing for Structure-based Anomaly DetectionCode0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly DetectionCode2
PIF: Anomaly detection via preference embedding—0
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders—0
Online Isolation ForestCode1
WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes—0
Few-Shot Anomaly-Driven Generation for Anomaly Classification and SegmentationCode2
Learning to Detect Multi-class Anomalies with Just One Normal Image PromptCode2
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-LearningCode2
Crowd Scene Analysis using Deep Learning Techniques—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Intelligent Road Anomaly Detection with Real-time Notification System for Enhanced Road Safety—0
Isolation Forest in Novelty Detection Scenario—0
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks—0
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph TransformerCode0
Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks—0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Vision Foundation Model Embedding-Based Semantic Anomaly Detection—0
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly DetectionCode2
EAGLE: Contrastive Learning for Efficient Graph 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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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