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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 2301–2350 of 4856 papers

TitleStatusHype
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles—0
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing—0
A convolutional neural network of low complexity for tumor anomaly detection—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation—0
Condition monitoring and anomaly detection in cyber-physical systems—0
Hybrid Open-set Segmentation with Synthetic Negative DataCode1
The role of noise in denoising models for anomaly detection in medical imagesCode1
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
Position Regression for Unsupervised Anomaly DetectionCode0
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection—0
Subgraph Centralization: A Necessary Step for Graph Anomaly DetectionCode1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese NetworksCode1
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Anomalies, Representations, and Self-Supervision—0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing—0
Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing—0
On Advantages of Mask-level Recognition for Outlier-aware SegmentationCode1
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Self-Supervised Video Forensics by Audio-Visual Anomaly DetectionCode1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly DetectionCode1
Removing Anomalies as Noises for Industrial Defect Localization—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—0
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks—0
Template-guided Hierarchical Feature Restoration for Anomaly Detection—0
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Feature Prediction Diffusion Model for Video Anomaly Detection—0
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction—0
Anomaly Detection using Score-based Perturbation ResilienceCode1
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping—0
Revisiting Reverse Distillation for Anomaly DetectionCode1
A principled distributional approach to trajectory similarity measurement—0
A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Application Of ADNN For Background Subtraction In Smart Surveillance System—0
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
PRISM: Privacy Preserving Healthcare Internet of Things Security Management—0
AER: Auto-Encoder with Regression for Time Series Anomaly DetectionCode3
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
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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