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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 2626–2650 of 4856 papers

TitleStatusHype
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study—0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies—0
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder—0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
Software Based Higher Order Structural Foot Abnormality Detection Using Image Processing—0
Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis—0
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
Fixing Inventory Inaccuracies At Scale—0
SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data—0
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders—0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch—0
Sparse Binary Transformers for Multivariate Time Series Modeling—0
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection—0
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image—0
Sparse Modelling for Feature Learning in High Dimensional Data—0
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking—0
Sparsity in Multivariate Extremes with Applications to Anomaly Detection—0
Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes—0
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques—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