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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 4051–4100 of 4856 papers

TitleStatusHype
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
Combining Machine Learning Models using combo LibraryCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Leveraging Log Instructions in Log-based Anomaly DetectionCode0
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet ArchitectureCode0
Individualized multi-horizon MRI trajectory prediction for Alzheimer's DiseaseCode0
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly DetectionCode0
Are We Using Autoencoders in a Wrong Way?Code0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
Learn Suspected Anomalies from Event Prompts for Video Anomaly DetectionCode0
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and InsightsCode0
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly DetectionCode0
Lightning Fast Video Anomaly Detection via Adversarial Knowledge DistillationCode0
Lightweight Collaborative Anomaly Detection for the IoT using BlockchainCode0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly GeneratorsCode0
Domain-independent detection of known anomaliesCode0
Link Analysis meets Ontologies: Are Embeddings the Answer?Code0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and BeyondCode0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
ARES: Locally Adaptive Reconstruction-based Anomaly ScoringCode0
DMAD: Dual Memory Bank for Real-World Anomaly DetectionCode0
STAN: Synthetic Network Traffic Generation with Generative Neural ModelsCode0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
Are generative deep models for novelty detection truly better?Code0
Statistical Analysis of Nearest Neighbor Methods for Anomaly DetectionCode0
Learning Temporal Regularity in Video SequencesCode0
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov ModelsCode0
Localizing Anomalies in Critical Infrastructure using Model-Based Drift ExplanationsCode0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
Localizing Anomalies via Multiscale Score Matching AnalysisCode0
Statistical Evaluation of Anomaly Detectors for SequencesCode0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous AttentionCode0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
Real-Time Anomaly Detection for Streaming AnalyticsCode0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Learning Representations for Time Series ClusteringCode0
Using Large-Scale Anomaly Detection on Code to Improve Kotlin CompilerCode0
TracInAD: Measuring Influence for Anomaly DetectionCode0
Learning Regularity in Skeleton Trajectories for Anomaly Detection in VideosCode0
Learning normal asymmetry representations for homologous brain structuresCode0
Anomaly detection with superexperts under delayed feedbackCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Wildfire danger prediction optimization with transfer learningCode0
Unsupervised Anomaly Detection through Mass Repulsing Optimal TransportCode0
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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