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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 2176–2200 of 4856 papers

TitleStatusHype
Anomaly detection using Diffusion-based methods—0
Constructing a meta-learner for unsupervised anomaly detection—0
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data—0
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression—0
GNN-based Anomaly Detection for Encoded Network Traffic—0
Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring—0
Constraining Anomaly Detection with Anomaly-Free Regions—0
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems—0
Constant Time EXPected Similarity Estimation using Stochastic Optimization—0
Anomaly Detection using Deep Learning based Image Completion—0
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Anomaly detection using data depth: multivariate case—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs—0
Conformalized Kernel Ridge Regression—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml—0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
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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