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 2626–2650 of 4856 papers

TitleStatusHype
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells—0
Neural Embedding: Learning the Embedding of the Manifold of Physics Data—0
Machine Learning with DBOS—0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection—0
SIAD: Self-supervised Image Anomaly Detection System—0
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization—0
Variational Autoencoders for Anomaly Detection in Respiratory Sounds—0
Convolutional Ensembling based Few-Shot Defect Detection Technique—0
Detecting Multivariate Time Series Anomalies with Zero Known LabelCode1
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series—0
Curved Geometric Networks for Visual Anomaly Recognition—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
DSR -- A dual subspace re-projection network for surface anomaly detectionCode1
A One-Class Classification method based on Expanded Non-Convex HullsCode0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers—0
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and SegmentationCode2
Look at Adjacent Frames: Video Anomaly Detection without Offline Training—0
Learning Appearance-motion Normality for Video Anomaly Detection—0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety—0
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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