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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 4551–4600 of 4856 papers

TitleStatusHype
Generating Synthetic X-ray Images of a Person from the Surface Geometry—0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection—0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows—0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Group Anomaly Detection using Deep Generative ModelsCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—0
Efficient Anomaly Detection via Matrix Sketching—0
Anomaly Detection for Industrial Big Data—0
Adaptive Cost-sensitive Online Classification—0
Correlated discrete data generation using adversarial training—0
Regional Priority Based Anomaly Detection using Autoencoders—0
Network Traffic Anomaly Detection Using Recurrent Neural NetworksCode0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents—0
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection—0
CIoTA: Collaborative IoT Anomaly Detection via Blockchain—0
Precision and Recall for Time SeriesCode0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM—0
Abnormality Detection in Mammography using Deep Convolutional Neural Networks—0
Analyzing Business Process Anomalies Using Autoencoders—0
Graph Laplacian for Image Anomaly DetectionCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Anomaly Detection using One-Class Neural NetworksCode0
Efficient GAN-Based Anomaly DetectionCode0
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web ApplicationsCode2
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models—0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Representation Learning for Resource Usage Prediction—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications—0
WEAC: Word embeddings for anomaly classification from event logs—0
Learning Deep Features for One-Class ClassificationCode0
Real-world Anomaly Detection in Surveillance VideosCode1
Detecting abnormal events in video using Narrowed Normality Clusters—0
Paranom: A Parallel Anomaly Dataset Generator—0
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videosCode0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Precision and Recall for Range-Based Anomaly Detection—0
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines—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
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