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 3801–3825 of 4856 papers

TitleStatusHype
Self-Supervised Transformers for Activity Classification using Ambient Sensors—0
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems—0
Preparing Weather Data for Real-Time Building Energy Simulation—0
Sub-clusters of Normal Data for Anomaly Detection—0
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome—0
Local Anomaly Detection in Videos using Object-Centric Adversarial Learning—0
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
Image Anomaly Detection by Aggregating Deep Pyramidal Representations—0
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning—0
Testing for Typicality with Respect to an Ensemble of Learned Distributions—0
Statistical learning for change point and anomaly detection in graphs—0
Self-Supervised Out-of-Distribution Detection in Brain CT Scans—0
Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance VideoCode0
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
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
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
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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