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 4026–4050 of 4856 papers

TitleStatusHype
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices—0
Semi-supervised Anomaly Detection on Attributed GraphsCode0
Time Series Data Augmentation for Deep Learning: A Survey—0
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments—0
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
Deep Nearest Neighbor Anomaly Detection—0
One-Class Graph Neural Networks for Anomaly Detection in Attributed NetworksCode1
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity DetectionCode1
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
A flexible outlier detector based on a topology given by graph communities—0
Correlation-aware Deep Generative Model for Unsupervised Anomaly DetectionCode1
Taurus: A Data Plane Architecture for Per-Packet ML—0
A review on outlier/anomaly detection in time series data—0
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data—0
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Iterative energy-based projection on a normal data manifold for anomaly localizationCode1
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Memory Augmented Generative Adversarial Networks for Anomaly Detection—0
movie2trailer: Unsupervised trailer generation using Anomaly detection—0
Anomaly Detection by One Class Latent Regularized Networks—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks—0
Unsupervised Anomaly Detection for X-Ray ImagesCode1
Ensemble Grammar Induction For Detecting Anomalies in Time Series—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