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 3476–3500 of 4856 papers

TitleStatusHype
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles—0
HR-Crime: Human-Related Anomaly Detection in Surveillance Videos—0
Margin-Aware Intra-Class Novelty Identification for Medical ImagesCode0
Anomaly Detection with Neural Parsers That Never Reject—0
Using Visual Anomaly Detection for Task Execution MonitoringCode0
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection—0
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs—0
Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation—0
HURRA! Human readable router anomaly detection—0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments—0
Anomaly Detection via Self-organizing MapCode0
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary—0
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—0
Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring—0
OnlineSTL: Scaling Time Series Decomposition by 100x—0
Explainable Debugger for Black-box Machine Learning ModelsCode0
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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