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 3951–4000 of 4856 papers

TitleStatusHype
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems—0
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder—0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
CIoTA: Collaborative IoT Anomaly Detection via Blockchain—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
Classification Tree Diagrams in Health Informatics Applications—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Client-Specific Anomaly Detection for Face Presentation Attack Detection—0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation—0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
Clustering-based Anomaly Detection for microservices—0
Clustering Driven Deep Autoencoder for Video Anomaly Detection—0
Clustering of Time Series Data with Prior Geographical Information—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection—0
Coincident Learning for Unsupervised Anomaly Detection—0
Collaborative Anomaly Detection—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems—0
Community-based anomaly detection using spectral graph filtering—0
Community-Level Anomaly Detection for Anti-Money Laundering—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System—0
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection—0
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers—0
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives—0
Comparison of RNN Encoder-Decoder Models for Anomaly Detection—0
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs—0
ComplexVAD: Detecting Interaction Anomalies in Video—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
Component-aware Unsupervised Logical Anomaly Generation for Industrial 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