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 3001–3025 of 4856 papers

TitleStatusHype
Dictionary Learning with Uniform Sparse Representations for Anomaly DetectionCode0
An Introduction to Autoencoders—0
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned ModelsCode1
Configurable Independent Component Analysis Preprocessing Accelerator—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
AnomMAN: Detect Anomaly on Multi-view Attributed Networks—0
Applications of Signature Methods to Market Anomaly Detection—0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra—0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis—0
Persistent Homology for Breast Tumor Classification using Mammogram Scans—0
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances—0
Latent Vector Expansion using Autoencoder for Anomaly Detection—0
Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation—0
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect MatchesCode1
Rethinking Video Anomaly Detection - A Continual Learning Approach—0
on the effectiveness of generative adversarial network on anomaly detectionCode0
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection—0
Monte Carlo EM for Deep Time Series Anomaly DetectionCode0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting DataCode1
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data—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