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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 1126–1150 of 4856 papers

TitleStatusHype
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models—0
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection—0
Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection—0
Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs—0
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising—0
An Origami-Inspired Endoscopic Capsule with Tactile Perception for Early Tissue Anomaly Detection—0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—0
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection—0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection—0
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey—0
Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniques—0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly DetectionCode0
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series RepresentationsCode1
Video Anomaly Detection in 10 Years: A Survey and Outlook—0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
Anomaly Detection by Context Contrasting—0
A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation—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