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 1426–1450 of 4856 papers

TitleStatusHype
Are Large Language Models Useful for Time Series Data Analysis?—0
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation—0
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP—0
Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational AutoencodersCode0
Learning Set Functions with Implicit Differentiation—0
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description—0
Know Unreported Roadway Incidents in Real-time: A Deep Learning Framework for Early Traffic Anomaly Detection—0
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing TasksCode0
Multimodal Industrial Anomaly Detection by Crossmodal Reverse DistillationCode0
Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Anomaly detection using Diffusion-based methods—0
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time SeriesCode0
siForest: Detecting Network Anomalies with Set-Structured Isolation Forest—0
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
Hyperedge Anomaly Detection with Hypergraph Neural NetworkCode0
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces—0
Anomaly Detection and Classification in Knowledge Graphs—0
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects—0
ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using Smartphone Sensors—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural 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