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 45764600 of 4856 papers

TitleStatusHype
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Anomaly Detection using One-Class Neural NetworksCode0
Efficient GAN-Based Anomaly DetectionCode0
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web ApplicationsCode2
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification0
Representation Learning for Resource Usage Prediction0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications0
WEAC: Word embeddings for anomaly classification from event logs0
Learning Deep Features for One-Class ClassificationCode0
Real-world Anomaly Detection in Surveillance VideosCode1
Detecting abnormal events in video using Narrowed Normality Clusters0
Paranom: A Parallel Anomaly Dataset Generator0
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videosCode0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection0
Precision and Recall for Range-Based Anomaly Detection0
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines0
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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