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 4226–4250 of 4856 papers

TitleStatusHype
Deep End-to-end Unsupervised Anomaly Detection—0
Combining Machine Learning Models using combo LibraryCode0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection—0
Fault-Diagnosing SLAM for Varying Scale Change Detection—0
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory—0
Anomaly Detection with Inexact Labels—0
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs—0
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise—0
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection—0
Image anomaly detection with capsule networks and imbalanced datasetsCode0
Future Frame Prediction Using Convolutional VRNN for Anomaly Detection—0
Subset Multivariate Collective And Point Anomaly Detection—0
Filtering Approaches for Dealing with Noise in Anomaly Detection—0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Deep Actor-Critic Reinforcement Learning for Anomaly Detection—0
Self-Supervised Representation Learning via Neighborhood-Relational Encoding—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach—0
Heterogeneous Relational Kernel Learning—0
A framework for anomaly detection using language modeling, and its applications to finance—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection—0
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans—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