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 3501–3550 of 4856 papers

TitleStatusHype
Multicalibrated Partitions for Importance Weights—0
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events—0
Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledgeCode1
Pixel-wise Anomaly Detection in Complex Driving ScenesCode1
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Anomaly Detection Based on Selection and Weighting in Latent Space—0
Student-Teacher Feature Pyramid Matching for Anomaly DetectionCode1
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network—0
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation—0
Neural Network-based Quantization for Network Automation—0
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection—0
Helicopter Track Identification with Autoencoder—0
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and AlgorithmsCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power linesCode0
Image/Video Deep Anomaly Detection: A Survey—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Online anomaly detection using statistical leverage for streaming business process eventsCode0
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised LearningCode1
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation—0
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Automatic Feature Extraction for Heartbeat Anomaly DetectionCode0
Railway Anomaly detection model using synthetic defect images generated by CycleGAN—0
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems—0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery—0
SliceNStitch: Continuous CP Decomposition of Sparse Tensor StreamsCode1
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models—0
Few-shot Network Anomaly Detection via Cross-network Meta-learningCode1
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
Interpretable Stability Bounds for Spectral Graph Filters—0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Topological Obstructions to Autoencoding—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Towards AIOps in Edge Computing Environments—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
Mutually exciting point process graphs for modelling dynamic networksCode0
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions—0
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Is Space-Time Attention All You Need for Video Understanding?Code2
Negative Data AugmentationCode1
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology AlignmentCode0
Exact Optimization of Conformal Predictors via Incremental and Decremental LearningCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation—0
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—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