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 3576–3600 of 4856 papers

TitleStatusHype
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Multi-Perspective Anomaly Detection—0
Graph Sanitation with Application to Node Classification—0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
DOC3-Deep One Class Classification using Contradictions—0
Theoretical Concept Study of Cooperative Abnormality Detection and Localization in Fluidic-Medium Molecular Communication—0
Understanding the Effect of Bias in Deep Anomaly DetectionCode0
Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection—0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
DoS and DDoS Mitigation Using Variational Autoencoders—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing—0
Anomaly Detection via Controlled Sensing and Deep Active Inference—0
Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: a case study in the capitals of Brazil—0
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
Good Practices and A Strong Baseline for Traffic Anomaly DetectionCode0
Energy-Based Anomaly Detection and Localization—0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs—0
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Discriminative-Generative Dual Memory Video Anomaly Detection—0
PANDA : Perceptually Aware Neural Detection of Anomalies—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