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 4501–4550 of 4856 papers

TitleStatusHype
Null Hypothesis Test for Anomaly DetectionCode0
Understanding the Effect of Bias in Deep Anomaly DetectionCode0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
The role of data embedding in quantum autoencoders for improved anomaly detectionCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time SeriesCode0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domainCode0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent FeaturesCode0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
An Adaptive Anaphylaxis Detection and Emergency Response SystemCode0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostCode0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator InterestCode0
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty DetectionCode0
Foundation Models for Structural Health MonitoringCode0
OML-AD: Online Machine Learning for Anomaly Detection in Time Series DataCode0
Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) AlgorithmsCode0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite ImagesCode0
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical SystemsCode0
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing DetectionCode0
Finite sample guarantees for quantile estimation: An application to detector threshold tuningCode0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
Few-shot Online Anomaly Detection and SegmentationCode0
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly ScoresCode0
Self-supervised learning for classifying paranasal anomalies in the maxillary sinusCode0
Fence GAN: Towards Better Anomaly DetectionCode0
Self-supervised Learning for Clustering of Wireless Spectrum ActivityCode0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Self-Supervised Learning for Detecting AI-Generated Faces as AnomaliesCode0
Deep generative models for unsupervised delamination detection using guided wavesCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
One Class Splitting Criteria for Random ForestsCode0
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional AutoencodersCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural NetworksCode0
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIMCode0
Feature space reduction as data preprocessing for the anomaly detectionCode0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly DetectionCode0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data StreamsCode0
Deep Anomaly Detection with Deviation NetworksCode0
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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