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

Showing 15511600 of 4856 papers

TitleStatusHype
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations0
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles0
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality0
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces0
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale0
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics0
A Comparison of Event Representations in DEFT0
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data0
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network0
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation0
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation0
Decentralized Federated Learning Preserves Model and Data Privacy0
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis0
Dealing with Imbalanced Classes in Bot-IoT Dataset0
Detecting abnormal events in video using Narrowed Normality Clusters0
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM0
Detecting Anomalies from Video-Sequences: a Novel Descriptor0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection0
AnomMAN: Detect Anomaly on Multi-view Attributed Networks0
Detecting Anomalies using Generative Adversarial Networks on Images0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection0
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction0
Anomize: Better Open Vocabulary Video Anomaly Detection0
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle0
Detecting Anomalous User Behavior in Remote Patient Monitoring0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis0
Detecting Attacks on IoT Devices using Featureless 1D-CNN0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection0
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data0
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing0
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions0
Detecting Contextual Network Anomalies with Graph Neural Networks0
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noise0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder0
Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation0
Detecting fake accounts through Generative Adversarial Network in online social media0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving0
Detecting Financial Market Manipulation with Statistical Physics Tools0
Detecting Gait Abnormalities in Foot-Floor Contacts During Walking Through Footstep-Induced Structural Vibrations0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified