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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 3401–3450 of 4856 papers

TitleStatusHype
Discriminative-Generative Dual Memory Video Anomaly Detection—0
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
PANDA : Perceptually Aware Neural Detection of Anomalies—0
Inpainting Transformer for Anomaly DetectionCode1
Multi-view Deep One-class Classification: A Systematic ExplorationCode0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Incident Detection on Junctions Using Image Processing—0
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach—0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Unsupervised Learning of Multi-level Structures for Anomaly Detection—0
The 5th AI City ChallengeCode1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
Anomaly Detection for Solder Joints Using β-VAECode1
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things—0
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder—0
Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis—0
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—0
Brittle Features May Help Anomaly Detection—0
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
An Efficient Approach for Anomaly Detection in Traffic Videos—0
Fine-grained Anomaly Detection via Multi-task Self-Supervision—0
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and LocalizationCode1
What is Wrong with One-Class Anomaly Detection?Code1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series—0
Autoencoders for unsupervised anomaly detection in high energy physics—0
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands—0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector—0
OneLog: Towards End-to-End Training in Software Log Anomaly Detection—0
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence—0
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees—0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance—0
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
Global Information Guided Video Anomaly Detection—0
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Mixed supervision for surface-defect detection: from weakly to fully supervised learningCode1
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance—0
Efficient Model Monitoring for Quality Control in Cardiac Image SegmentationCode0
Using a Neural Network to Detect Anomalies given an N-gram Profile—0
LearningCity: Knowledge Generation for Smart Cities—0
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language ProcessingCode1
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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