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 926–950 of 4856 papers

TitleStatusHype
Time Series Analysis for Education: Methods, Applications, and Future DirectionsCode1
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Variational Autoencoder for Anomaly Detection: A Comparative StudyCode1
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer—0
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations—0
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological AnalysisCode1
UMAD: University of Macau Anomaly Detection Benchmark DatasetCode1
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization—0
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control—0
Physics-Driven AI Correction in Laser Absorption Sensing Quantification—0
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Deep Generative Classification of Blood Cell MorphologyCode1
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer—0
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment PerspectiveCode0
HELP: Hierarchical Embeddings-based Log Parsing—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Latent Anomaly Detection Through Density Matrices—0
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series AnalysisCode2
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
Investigation of unsupervised and supervised hyperspectral anomaly detection—0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling—0
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series 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