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 4051–4075 of 4856 papers

TitleStatusHype
SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection—0
One-Shot GAN Generated Fake Face Detection—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
MIM-Based GAN: Information Metric to Amplify Small Probability Events Importance in Generative Adversarial Networks—0
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems—0
Attention-Based Self-Supervised Feature Learning for Security Data—0
robROSE: A robust approach for dealing with imbalanced data in fraud detectionCode0
Breast Cancer Detection Using Convolutional Neural NetworksCode0
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing—0
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models—0
Anomalous Example Detection in Deep Learning: A Survey—0
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection—0
Hybrid Cryptocurrency Pump and Dump Detection—0
Building and Interpreting Deep Similarity ModelsCode0
Anomaly Detection in Beehives using Deep Recurrent Autoencoders—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Machine Learning based Anomaly Detection for 5G Networks—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
Why is the Mahalanobis Distance Effective for Anomaly Detection?—0
Unsupervised Dictionary Learning for Anomaly Detection—0
DROCC: Deep Robust One-Class Classification—0
Time Series Data Augmentation for Deep Learning: A Survey—0
Semi-supervised Anomaly Detection on Attributed GraphsCode0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices—0
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments—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