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 1826–1850 of 4856 papers

TitleStatusHype
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural NetworksCode1
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss AmplificationCode0
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory ClassificationCode0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
Bias in Unsupervised Anomaly Detection in Brain MRI—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data—0
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs—0
Burnt area extraction from high-resolution satellite images based on anomaly detection—0
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation MethodologyCode0
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection—0
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models—0
REB: Reducing Biases in Representation for Industrial Anomaly DetectionCode1
Low-count Time Series Anomaly Detection—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly DetectionCode2
Class Label-aware Graph Anomaly DetectionCode1
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly DetectionCode1
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
Inferring Power Grid Information with Power Line Communications: Review and Insights—0
Adaptive Thresholding Heuristic for KPI Anomaly DetectionCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for 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