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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 1401–1450 of 4856 papers

TitleStatusHype
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event DetectionCode0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection LibraryCode0
An anomaly detection approach for backdoored neural networks: face recognition as a case studyCode0
Link Analysis meets Ontologies: Are Embeddings the Answer?Code0
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
Comparison of Anomaly Detectors: Context MattersCode0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
Foundation Models for Structural Health MonitoringCode0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite ImagesCode0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Combining Machine Learning Models using combo LibraryCode0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial NetworksCode0
Few-shot Online Anomaly Detection and SegmentationCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance VideoCode0
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical ReasoningCode0
Fence GAN: Towards Better Anomaly DetectionCode0
CNTS: Cooperative Network for Time SeriesCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Cluster-Wide Task Slowdown Detection in Cloud SystemCode0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly DetectionCode0
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing TasksCode0
Fast Particle-based Anomaly Detection Algorithm with Variational AutoencoderCode0
Feature space reduction as data preprocessing for the anomaly detectionCode0
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objectsCode0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User AnomaliesCode0
CloudShield: Real-time Anomaly Detection in the CloudCode0
Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly MachineCode0
Achieving Counterfactual Fairness for Anomaly DetectionCode0
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
Margin-Aware Intra-Class Novelty Identification for Medical ImagesCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
FADE: Forecasting for Anomaly Detection on ECGCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
FABLE : Fabric Anomaly Detection Automation ProcessCode0
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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