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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 3101–3150 of 4856 papers

TitleStatusHype
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition—0
PAC-Wrap: Semi-Supervised PAC Anomaly Detection—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTMCode0
Unsupervised Features Ranking via Coalitional Game Theory for Categorical DataCode0
Federated Anomaly Detection over Distributed Data Streams—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature—0
Anomaly Detection in Intra-Vehicle Networks—0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding—0
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training—0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection—0
Explainable multi-class anomaly detection on functional data—0
A Contrario multi-scale anomaly detection method for industrial quality inspection—0
Object Class Aware Video Anomaly Detection through Image Translation—0
TracInAD: Measuring Influence for Anomaly DetectionCode0
ADDAI: Anomaly Detection using Distributed AI—0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting—0
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection—0
Novel Applications for VAE-based Anomaly Detection Systems—0
IRC-safe Graph Autoencoder for unsupervised anomaly detection—0
Topological Data Analysis for Anomaly Detection in Host-Based Logs—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection—0
NLP Based Anomaly Detection for Categorical Time Series—0
Feature anomaly detection system (FADS) for intelligent manufacturing—0
Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks—0
Scalable Motif Counting for Large-scale Temporal GraphsCode0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors—0
AI for human assessment: What do professional assessors need?—0
AFSC: Adaptive Fourier Space Compression for Anomaly Detection—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data—0
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization—0
Self-Supervised Losses for One-Class Textual Anomaly Detection—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