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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 4856 papers

TitleStatusHype
Federated Foundation Models on Heterogeneous Time SeriesCode1
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection—0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Unlocking the Potential of Reverse Distillation for Anomaly DetectionCode1
Anomaly detection using Diffusion-based methods—0
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time SeriesCode0
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
siForest: Detecting Network Anomalies with Set-Structured Isolation Forest—0
Hyperedge Anomaly Detection with Hypergraph Neural NetworkCode0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection—0
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces—0
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous DrivingCode1
Anomaly Detection and Classification in Knowledge Graphs—0
ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using Smartphone Sensors—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects—0
Towards Zero-shot 3D Anomaly Localization—0
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—0
SCADE: Scalable Framework for Anomaly Detection in High-Performance System—0
ONER: Online Experience Replay for Incremental 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