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 651–700 of 4856 papers

TitleStatusHype
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literatureCode1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly DetectionCode1
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time SeriesCode1
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case StudyCode1
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human DiseaseCode1
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
Continuous Memory Representation for Anomaly DetectionCode1
Masked Contrastive Learning for Anomaly DetectionCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly DetectionsCode1
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and BenchmarkCode1
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIsCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Anomaly Detection in Medical Imaging with Deep Perceptual AutoencodersCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionCode1
Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly DetectionCode1
Beyond Individual Input for Deep Anomaly Detection on Tabular DataCode1
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low ToleranceCode1
Graph Neural Networks based Log Anomaly Detection and ExplanationCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
Holistic Representation Learning for Multitask Trajectory Anomaly DetectionCode1
Deep and Confident Prediction for Time Series at UberCode1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly DetectionCode1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
Incorporating Feedback into Tree-based Anomaly DetectionCode1
Anomaly Detection with Score Distribution DiscriminationCode1
Deep Anomaly Detection on Attributed NetworksCode1
Deep Anomaly Detection Using Geometric TransformationsCode1
Mixed supervision for surface-defect detection: from weakly to fully supervised learningCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
Deep Anomaly Detection with Outlier ExposureCode1
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition SoundsCode1
A SAM-guided Two-stream Lightweight Model for Anomaly DetectionCode1
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly DetectionCode1
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly DetectionCode1
Graph Convolutional Networks for traffic anomalyCode1
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
Deep Generative Classification of Blood Cell MorphologyCode1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
Deep Graph-level Anomaly Detection by Glocal Knowledge DistillationCode1
Anomaly localization by modeling perceptual featuresCode1
Anomaly Detection in Emails using Machine Learning and Header InformationCode1
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop LearningCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
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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