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 1251–1275 of 4856 papers

TitleStatusHype
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
IPAD: Industrial Process Anomaly Detection Dataset—0
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance—0
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing—0
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection—0
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories—0
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models—0
Warped Time Series Anomaly Detection—0
Blind Localization and Clustering of Anomalies in TexturesCode1
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—0
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark—0
CARE to Compare: A real-world dataset for anomaly detection in wind turbine data—0
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices ApproachCode0
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time SeriesCode0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora—0
Fault Detection in Mobile Networks Using Diffusion Models—0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and DiscriminationCode1
TSLANet: Rethinking Transformers for Time Series Representation LearningCode3
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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