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 3276–3300 of 4856 papers

TitleStatusHype
Theoretical Concept Study of Cooperative Abnormality Detection and Localization in Fluidic-Medium Molecular Communication—0
The Power of Adaptivity in Identifying Statistical Alternatives—0
The Profiling Machine: Active Generalization over Knowledge—0
The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era—0
The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey—0
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description—0
The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time—0
Through-Foliage Tracking with Airborne Optical Sectioning—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies—0
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model—0
Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection Applied to the Environmental Field—0
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement—0
Timeseria: an object-oriented time series processing library—0
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Time Series Anomaly Detection for Smart Grids: A Survey—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT—0
Time Series Anomaly Detection with label-free Model Selection—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
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