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 851–900 of 4856 papers

TitleStatusHype
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly DetectionCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly DetectionCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Automating Outlier Detection via Meta-LearningCode1
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop LearningCode1
Learning Neural Set Functions Under the Optimal Subset OracleCode1
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly DetectionCode1
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative StudyCode1
Auto-Encoding Variational BayesCode1
Graph Convolutional Networks for traffic anomalyCode1
A Survey of Visual Sensory Anomaly DetectionCode1
A Survey of World Models for Autonomous DrivingCode1
Graph-level Anomaly Detection via Hierarchical Memory NetworksCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low ToleranceCode1
Learning Memory-guided Normality for Anomaly DetectionCode1
Learning Not to Reconstruct AnomaliesCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
Learning a Cross-modality Anomaly Detector for Remote Sensing ImageryCode1
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor DetectionCode1
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and EvaluationCode1
Continuous Memory Representation for Anomaly DetectionCode1
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentCode1
Attention-based residual autoencoder for video anomaly detectionCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly DetectionCode1
Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionCode1
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal AttentionCode1
Active Anomaly Detection via EnsemblesCode1
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational AutoencoderCode1
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case StudyCode1
Learning Generalized Spoof Cues for Face Anti-spoofingCode1
How To Backdoor Federated LearningCode1
Anomaly Detection in Aerial Videos with TransformersCode1
An Attribute-based Method for Video Anomaly DetectionCode1
HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-netCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
Learning Latent Space Energy-Based Prior ModelCode1
Hybrid Open-set Segmentation with Synthetic Negative DataCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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