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 1301–1325 of 4856 papers

TitleStatusHype
Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning—0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—0
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New BaselineCode1
Harnessing Large Language Models for Training-free Video Anomaly Detection—0
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems—0
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security—0
On the True Distribution Approximation of Minimum Bayes-Risk DecodingCode0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
Long-Tailed Anomaly Detection with Learnable Class Names—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
Road Obstacle Detection based on Unknown Objectness Scores—0
Few-shot Online Anomaly Detection and SegmentationCode0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis—0
Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed TomographyCode3
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework—0
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data—0
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components—0
A task of anomaly detection for a smart satellite Internet of things system—0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
SoftPatch: Unsupervised Anomaly Detection with Noisy DataCode2
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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