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 2326–2350 of 4856 papers

TitleStatusHype
An Incremental Clustering Method for Anomaly Detection in Flight Data—0
Detection and Statistical Modeling of Birth-Death Anomaly—0
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code—0
A review on outlier/anomaly detection in time series data—0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning—0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach—0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies—0
A convolutional neural network of low complexity for tumor anomaly detection—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
A Review of Open Source Software Tools for Time Series Analysis—0
A review of neural network algorithms and their applications in supercritical extraction—0
Detecting Relative Anomaly—0
A Review of Machine Learning based Anomaly Detection Techniques—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation—0
A Review of Computer Vision Methods in Network Security—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs—0
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models—0
Detecting out-of-context objects using contextual cues—0
Detecting Novelties with Empty Classes—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
Detecting Log Anomalies with Multi-Head Attention (LAMA)—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