SOTAVerified

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 2351–2400 of 4856 papers

TitleStatusHype
Classification of Anomalies in Telecommunication Network KPI Time Series—0
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
Group Anomaly Detection using Flexible Genre Models—0
Grouped Convolutional Neural Networks for Multivariate Time Series—0
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection—0
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks—0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection—0
Anomaly Detection in Trajectory Data with Normalizing Flows—0
Client-Specific Anomaly Detection for Face Presentation Attack Detection—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction—0
Graph-Level Embedding for Time-Evolving Graphs—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection—0
Harnessing Large Language Models for Training-free Video Anomaly Detection—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection—0
iMedImage Technical Report—0
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—0
Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection—0
Sobolev Space Regularised Pre Density Models—0
Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network—0
ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
Helicopter Track Identification with Autoencoder—0
HELP: Hierarchical Embeddings-based Log Parsing—0
CIoTA: Collaborative IoT Anomaly Detection via Blockchain—0
Image/Video Deep Anomaly Detection: A Survey—0
Heterogeneous Noisy Short Signal Camouflage in Multi-Domain Environment Decision-Making—0
Heterogeneous Relational Kernel Learning—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning—0
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection—0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Clustering of Time Series Data with Prior Geographical Information—0
Imbalanced Aircraft Data Anomaly Detection—0
Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—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