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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 4351–4400 of 4856 papers

TitleStatusHype
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Reducing Anomaly Detection in Images to Detection in Noise—0
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
Deep Representation Learning for Social Network Analysis—0
Deep Anomaly Detection for Generalized Face Anti-SpoofingCode0
Temporal Cycle-Consistency LearningCode0
Graph-Based Method for Anomaly Prediction in Brain Network—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators—0
Software Based Higher Order Structural Foot Abnormality Detection Using Image Processing—0
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery—0
Deep Learning for System Trace Restoration—0
Place-specific Background Modeling Using Recursive Autoencoders—0
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic SegmentationCode0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Efficient GAN-based method for cyber-intrusion detection—0
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly DetectionCode0
Online Topology Identification from Vector Autoregressive Time SeriesCode0
Using Google Analytics to Support Cybersecurity Forensics—0
Active Learning for Network Intrusion Detection—0
Fence GAN: Towards Better Anomaly DetectionCode0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models—0
Robust Subspace Recovery Layer for Unsupervised Anomaly DetectionCode0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
Spatially-weighted Anomaly Detection with Regression Model—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
Ensemble Clustering for Graphs: Comparisons and ApplicationsCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
Learning Competitive and Discriminative Reconstructions for Anomaly Detection—0
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly DetectionCode0
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning—0
ADS-ME: Anomaly Detection System for Micro-expression Spotting—0
Learning Regularity in Skeleton Trajectories for Anomaly Detection in VideosCode0
Improving SIEM for Critical SCADA Water Infrastructures Using Machine LearningCode0
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detectionCode0
Deep Generative Design: Integration of Topology Optimization and Generative Models—0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders—0
Towards Corner Case Detection for Autonomous Driving—0
Anomaly Detection for an E-commerce Pricing System—0
Bayesian Anomaly Detection and Classification—0
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks—0
Anomaly Detection with Adversarial Dual AutoencodersCode0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming DataCode0
Street Scene: A new dataset and evaluation protocol for video anomaly detection—0
KINN: Incorporating Expert Knowledge in Neural Networks—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