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 3676–3700 of 4856 papers

TitleStatusHype
Self-Supervised Out-of-Distribution Detection in Brain CT Scans—0
Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance VideoCode0
Building an Automated and Self-Aware Anomaly Detection SystemCode1
F-FADE: Frequency Factorization for Anomaly Detection in Edge StreamsCode1
Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks—0
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction—0
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Video Generative Adversarial Networks: A Review—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Autoencoding Features for Aviation Machine Learning Problems—0
Unsupervised Anomaly Detection in Parole Hearings using Language Models—0
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test—0
A review of neural network algorithms and their applications in supercritical extraction—0
Higher-Order Moment-Based Anomaly Detection—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems—0
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection—0
Dynamic Bayesian Approach for decision-making in Ego-Things—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—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