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 4501–4525 of 4856 papers

TitleStatusHype
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Exploring time-series motifs through DTW-SOM—0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection—0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks—0
Anomaly Detection with Neural Parsers That Never Reject—0
Extracting Information from Indian First Names—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation—0
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection—0
Factor Analysis of Mixed Data for Anomaly Detection—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks—0
Fair Anomaly Detection For Imbalanced Groups—0
Faithful Explanations for Deep Graph Models—0
Fast Adaptive Anomaly Detection—0
Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection—0
Fast Deep Autoencoder for Federated learning—0
Fast kernel half-space depth for data with non-convex supports—0
Show:102550
← PrevPage 181 of 195Next →
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