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 1151–1175 of 4856 papers

TitleStatusHype
Anomaly detection and classification for streaming data using PDEs—0
A Systematic Mapping Study in AIOps—0
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows—0
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network—0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
A Survey on Time-Series Distance Measures—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
A Survey on Social Media Anomaly Detection—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Active Anomaly Detection for time-domain discoveries—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it—0
A Survey on Graph Representation Learning Methods—0
Abnormal component analysis—0
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—0
A Survey on Explainable Anomaly Detection—0
A Survey on Embedding Dynamic Graphs—0
Show:102550
← PrevPage 47 of 195Next →
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