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 3576–3600 of 4856 papers

TitleStatusHype
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery—0
Deep unsupervised anomaly detection—0
Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection—0
A Survey on Embedding Dynamic Graphs—0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies—0
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing—0
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos—0
Learning Unsupervised Metaformer for Anomaly Detection—0
DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly DetectionCode1
Road Anomaly Detection by Partial Image Reconstruction With Segmentation CouplingCode1
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection—0
Anomaly detection and regime searching in fitness-tracker data—0
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection—0
Learning Deep Latent Variable Models via Amortized Langevin Dynamics—0
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC GuaranteesCode0
Domain Adaptation via Anaomaly Detection—0
Anomaly detection in dynamical systems from measured time series—0
Data Transformer for Anomalous Trajectory Detection—0
Unsupervised Anomaly Detection by Robust Collaborative AutoencodersCode1
A General Framework for Unsupervised Anomaly Detection—0
Dynamic Graph-Based Anomaly Detection in the Electrical GridCode1
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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