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 4301–4350 of 4856 papers

TitleStatusHype
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkCode0
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip StatisticsCode0
Detecting Irregular Network Activity with Adversarial Learning and Expert FeedbackCode0
Hyperedge Anomaly Detection with Hypergraph Neural NetworkCode0
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encodersCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
Monte Carlo EM for Deep Time Series Anomaly DetectionCode0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric RepresentationCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Detecting Anomalous Events in Object-centric Business Processes via Graph Neural NetworksCode0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Anomaly Detection with Variance Stabilized Density EstimationCode0
Breast Cancer Detection Using Convolutional Neural NetworksCode0
Detecting Anomalies in Image Classification by Means of Semantic RelationshipsCode0
Hybrid Deep Network for Anomaly DetectionCode0
Human Kinematics-inspired Skeleton-based Video Anomaly DetectionCode0
MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and SystemsCode0
robROSE: A robust approach for dealing with imbalanced data in fraud detectionCode0
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection DatasetCode0
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?Code0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Anomaly Detection with Selective Dictionary LearningCode0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Explainable Contextual Anomaly Detection using Quantile Regression ForestsCode0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel ApproachCode0
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view informationCode0
Adaptive Thresholding Heuristic for KPI Anomaly DetectionCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Anomaly Detection with Robust Deep AutoencodersCode0
Braced Fourier Continuation and Regression for Anomaly DetectionCode0
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection SimplifiedCode0
High-dimensional and Permutation Invariant Anomaly DetectionCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming DataCode0
Adaptive Deviation Learning for Visual Anomaly Detection with Data ContaminationCode0
Robust, Deep and Inductive Anomaly DetectionCode0
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly DetectionCode0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
Hashing for Structure-based Anomaly DetectionCode0
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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