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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 801–850 of 4856 papers

TitleStatusHype
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Reconstruction by Inpainting for Visual Anomaly DetectionCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm RateCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly DetectionCode1
Automating Outlier Detection via Meta-LearningCode1
Real-Time Anomaly Detection in Edge StreamsCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Generator Versus Segmentor: Pseudo-healthy SynthesisCode1
Understanding Coarsening for Embedding Large-Scale GraphsCode1
PySAD: A Streaming Anomaly Detection Framework in PythonCode1
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noiseCode1
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing FlowsCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile RobotsCode1
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
Localizing Anomalies from Weakly-Labeled VideosCode1
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Anomaly localization by modeling perceptual featuresCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
DeScarGAN: Disease-Specific Anomaly Detection with Weak SupervisionCode1
Fast Unsupervised Anomaly Detection in Traffic VideosCode1
Weakly and Partially Supervised Learning Frameworks for Anomaly DetectionCode1
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportanceCode1
Backpropagated Gradient Representations for Anomaly DetectionCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Few-shot Scene-adaptive Anomaly DetectionCode1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
Anomaly Detection-Based Unknown Face Presentation Attack DetectionCode1
Few-Shot One-Class Classification via Meta-LearningCode1
Explainable Deep One-Class ClassificationCode1
Laplacian Change Point Detection for Dynamic GraphsCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Patch SVDD: Patch-level SVDD for Anomaly Detection and SegmentationCode1
Few-Shot Anomaly Detection for Polyp Frames from ColonoscopyCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
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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