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 1176–1200 of 4856 papers

TitleStatusHype
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
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
Active anomaly detection based on deep one-class classification—0
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation—0
A Survey on Deep Learning Techniques for Video Anomaly Detection—0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Adversarially learned anomaly detection for time series data—0
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
A Survey of Single-Scene Video Anomaly Detection—0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Anomaly Awareness—0
Activation Learning by Local Competitions—0
Curved Geometric Networks for Visual Anomaly Recognition—0
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition—0
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation—0
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
A Survey of Anomaly Detection in In-Vehicle Networks—0
A Survey of Anomaly Detection in Cyber-Physical Systems—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—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