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 2201–2250 of 4856 papers

TitleStatusHype
Meta-survey on outlier and anomaly detectionCode0
Detecting Contextual Network Anomalies with Graph Neural Networks—0
Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers—0
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection—0
Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything—0
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection—0
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
A brief introduction to a framework named Multilevel Guidance-Exploration NetworkCode0
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning—0
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization—0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection—0
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity MappingCode0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis—0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems—0
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing—0
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series—0
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly DetectionCode0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety—0
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations—0
ADT: Agent-based Dynamic Thresholding for Anomaly Detection—0
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach—0
Revisiting Non-separable Binary Classification and its Applications in Anomaly DetectionCode0
Bagged Regularized k-Distances for Anomaly Detection—0
Online Anomaly Detection over Live Social Video Streaming—0
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
Unsupervised textile defect detection using convolutional neural networks—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
Fast Particle-based Anomaly Detection Algorithm with Variational AutoencoderCode0
Diagnosis driven Anomaly Detection for CPS—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly DetectionCode0
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection—0
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Anomaly detection in cross-country money transfer temporal networks—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
Video Anomaly Detection using GAN—0
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators—0
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance InspectionCode0
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET—0
Correlated Attention in Transformers for Multivariate Time Series—0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data—0
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—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