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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 3501–3550 of 4856 papers

TitleStatusHype
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications—0
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders—0
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage—0
Unsupervised Two-Stage Anomaly Detection—0
Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features—0
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection—0
Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities—0
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning—0
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection—0
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality—0
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence—0
Using a Neural Network to Detect Anomalies given an N-gram Profile—0
Using anomaly detection to support classification of fast running (packaging) processes—0
Using Bursty Announcements for Detecting BGP Routing Anomalies—0
Using Causality for Enhanced Prediction of Web Traffic Time Series—0
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach—0
Using Ensemble Classifiers to Detect Incipient Anomalies—0
Using Google Analytics to Support Cybersecurity Forensics—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation—0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories—0
VALD-GAN: video anomaly detection using latent discriminator augmented GAN—0
Variational autoencoder-based neural network model compression—0
Variational Autoencoders for Anomaly Detection in Respiratory Sounds—0
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs—0
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Variation and generality in encoding of syntactic anomaly information in sentence embeddings—0
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models—0
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Video Anomaly Detection and Explanation via Large Language Models—0
Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression—0
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder—0
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
Video Anomaly Detection for Smart Surveillance—0
Video Anomaly Detection in 10 Years: A Survey and Outlook—0
Video Anomaly Detection using GAN—0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining—0
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange—0
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks—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