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 3876–3900 of 4856 papers

TitleStatusHype
A Unifying Review of Deep and Shallow Anomaly Detection—0
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering—0
Out-Of-Bag Anomaly Detection—0
Unsupervised Anomaly Detection on Temporal Multiway Data—0
A deep learning-based approach for the automated surface inspection of copper clad laminate images—0
On Multi-Session Website Fingerprinting over TLS Handshake—0
PANDA: Predicting the change in proteins binding affinity upon mutations using sequence informationCode0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA—0
Machine Learning Applications in Misuse and Anomaly Detection—0
Label-Free Segmentation of COVID-19 Lesions in Lung CT—0
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System—0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography—0
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation—0
Simulation-Assisted Decorrelation for Resonant Anomaly DetectionCode0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Anomaly Detection by Recombining Gated Unsupervised ExpertsCode0
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly DetectionCode0
Puzzle-AE: Novelty Detection in Images through Solving PuzzlesCode0
GAN-based Anomaly Detection in Imbalance Problems—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
The Impact of Discretization Method on the Detection of Six Types of Anomalies in DatasetsCode0
Training robust anomaly detection using ML-Enhanced simulations—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