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 3651–3675 of 4856 papers

TitleStatusHype
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions—0
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction—0
LogLLaMA: Transformer-based log anomaly detection with LLaMA—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
LogRCA: Log-based Root Cause Analysis for Distributed Services—0
LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis—0
Long-Tailed Anomaly Detection with Learnable Class Names—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
Look at Adjacent Frames: Video Anomaly Detection without Offline Training—0
Looking for Tiny Defects via Forward-Backward Feature Transfer—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Low-count Time Series Anomaly Detection—0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood—0
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data—0
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection—0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding—0
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory—0
LSTM-based Anomaly Detection for Non-linear Dynamical System—0
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems—0
Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection—0
M3A: Model, MetaModel, and Anomaly Detection in Web Searches—0
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising—0
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection—0
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA—0
Machine Learning Applications in Misuse and 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