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

Showing 37513775 of 4856 papers

TitleStatusHype
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly DetectionCode1
A Unifying Review of Deep and Shallow Anomaly Detection0
Automating Outlier Detection via Meta-LearningCode1
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering0
Out-Of-Bag Anomaly Detection0
Unsupervised Anomaly Detection on Temporal Multiway Data0
A deep learning-based approach for the automated surface inspection of copper clad laminate images0
On Multi-Session Website Fingerprinting over TLS Handshake0
Real-Time Anomaly Detection in Edge StreamsCode1
Large-Scale Intelligent MicroservicesCode3
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
PANDA: Predicting the change in proteins binding affinity upon mutations using sequence informationCode0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model0
TadGAN: Time Series Anomaly Detection Using Generative Adversarial NetworksCode2
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Generator Versus Segmentor: Pseudo-healthy SynthesisCode1
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA0
Machine Learning Applications in Misuse and Anomaly Detection0
Understanding Coarsening for Embedding Large-Scale GraphsCode1
Label-Free Segmentation of COVID-19 Lesions in Lung CT0
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography0
PySAD: A Streaming Anomaly Detection Framework in PythonCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified