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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 4856 papers

TitleStatusHype
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Deep Anomaly Detection on Tennessee Eastman Process Data—0
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus—0
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Deep Anomaly Detection in Text—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Deep evolving semi-supervised anomaly detection—0
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement—0
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems—0
Deep Anomaly Detection by Residual Adaptation—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection—0
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity—0
An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection—0
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing—0
Deep Actor-Critic Reinforcement Learning for Anomaly Detection—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate 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
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