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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 2551–2575 of 4856 papers

TitleStatusHype
A Robust Likelihood Model for Novelty Detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
PV Fleet Modeling via Smooth Periodic Gaussian Copula—0
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder—0
Anomaly Detection Techniques in Smart Grid Systems: A Review—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
A Hybrid Approach for Smart Alert Generation—0
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting—0
Graph-Level Embedding for Time-Evolving Graphs—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
Anomaly Detection with Variance Stabilized Density EstimationCode0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16—0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection—0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
Vision Transformers for Small Histological Datasets Learned through Knowledge DistillationCode0
Single-Model Attribution of Generative Models Through Final-Layer InversionCode0
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection—0
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series—0
Anomaly Detection in Satellite Videos using Diffusion Models—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
Real time dense anomaly detection by learning on synthetic negative data—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