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 1051–1075 of 4856 papers

TitleStatusHype
Looking for Tiny Defects via Forward-Backward Feature Transfer—0
SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly DetectionCode1
Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial FlowsCode0
Domain-independent detection of known anomaliesCode0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization—0
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining TasksCode0
LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log AnalysisCode1
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly DetectionCode1
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based ModelsCode1
The OPS-SAT benchmark for detecting anomalies in satellite telemetryCode1
Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection—0
Real-Time Energy Measurement for Non-Intrusive Well-Being Monitoring of Elderly People -- a Case Study—0
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly DetectionCode0
ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural BehaviorCode0
Localizing Anomalies via Multiscale Score Matching AnalysisCode0
Odd-One-Out: Anomaly Detection by Comparing with NeighborsCode2
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems—0
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly DetectionCode1
xSemAD: Explainable Semantic Anomaly Detection in Event Logs Using Sequence-to-Sequence ModelsCode0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation—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