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 2951–2975 of 4856 papers

TitleStatusHype
vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection—0
An Automated Analysis Framework for Trajectory Datasets—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
Detecting out-of-context objects using contextual cues—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data—0
Log-based Anomaly Detection with Deep Learning: How Far Are We?Code2
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Time Series Anomaly Detection by Cumulative Radon FeaturesCode1
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting—0
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Robust Anomaly Detection for Time-series Data—0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Robust Audio Anomaly Detection—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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