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 4201–4225 of 4856 papers

TitleStatusHype
Future Frame Prediction Using Convolutional VRNN for Anomaly Detection—0
Subset Multivariate Collective And Point Anomaly Detection—0
Filtering Approaches for Dealing with Noise in Anomaly Detection—0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Deep Actor-Critic Reinforcement Learning for Anomaly Detection—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
Self-Supervised Representation Learning via Neighborhood-Relational Encoding—0
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach—0
A framework for anomaly detection using language modeling, and its applications to finance—0
Heterogeneous Relational Kernel Learning—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection—0
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans—0
WiFi Motion Detection: A Study into Efficacy and Classification—0
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection—0
Detection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems—0
Hybrid Deep Network for Anomaly DetectionCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Detecting semantic anomaliesCode0
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection—0
Anomaly Detection in High Dimensional DataCode0
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks—0
Deep Structured Cross-Modal 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
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