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 18011850 of 4856 papers

TitleStatusHype
Reasonable Anomaly Detection in Long SequencesCode0
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source0
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability0
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain TestingCode1
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems0
Towards frugal unsupervised detection of subtle abnormalities in medical imagingCode0
Prior Knowledge Guided Network for Video Anomaly Detection0
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature RankingCode1
Are We Using Autoencoders in a Wrong Way?Code0
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection0
Anomaly detection with semi-supervised classification based on risk estimators0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter0
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRICode1
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality0
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly LocalizationCode1
Classification of Anomalies in Telecommunication Network KPI Time Series0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification0
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models0
A Comprehensive Augmentation Framework for Anomaly Detection0
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly DetectionCode1
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly DetectionCode0
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language ModelsCode3
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities0
Tackling Diverse Minorities in Imbalanced Classification0
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural NetworksCode1
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss AmplificationCode0
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory ClassificationCode0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy0
Bias in Unsupervised Anomaly Detection in Brain MRI0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data0
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs0
Burnt area extraction from high-resolution satellite images based on anomaly detection0
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation MethodologyCode0
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection0
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models0
REB: Reducing Biases in Representation for Industrial Anomaly DetectionCode1
Low-count Time Series Anomaly Detection0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection0
Few-shot Anomaly Detection in Text with Deviation Learning0
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly DetectionCode2
Class Label-aware Graph Anomaly DetectionCode1
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection0
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly DetectionCode1
Label-based Graph Augmentation with Metapath for Graph Anomaly DetectionCode0
Inferring Power Grid Information with Power Line Communications: Review and Insights0
Adaptive Thresholding Heuristic for KPI Anomaly DetectionCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection0
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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
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (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