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 2226–2250 of 4856 papers

TitleStatusHype
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients—0
MDF-Net for abnormality detection by fusing X-rays with clinical dataCode1
Interruptions detection in video conferencesCode0
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical SystemsCode0
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection—0
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
Set Features for Fine-grained Anomaly DetectionCode1
Explainable Contextual Anomaly Detection using Quantile Regression ForestsCode0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Memory-augmented Online Video Anomaly DetectionCode1
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection—0
CNTS: Cooperative Network for Time SeriesCode0
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine—0
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Collaborative Discrepancy Optimization for Reliable Image Anomaly LocalizationCode1
A method for incremental discovery of financial event types based on anomaly detection—0
Zero-Shot Anomaly Detection via Batch NormalizationCode1
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—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