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

Showing 28012825 of 4856 papers

TitleStatusHype
Explainable multi-class anomaly detection on functional data0
Object Class Aware Video Anomaly Detection through Image Translation0
Multimodal Detection of Unknown Objects on Roads for Autonomous DrivingCode1
TracInAD: Measuring Influence for Anomaly DetectionCode0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection0
ADDAI: Anomaly Detection using Distributed AI0
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiesCode2
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANsCode1
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection0
IRC-safe Graph Autoencoder for unsupervised anomaly detection0
Novel Applications for VAE-based Anomaly Detection Systems0
Topological Data Analysis for Anomaly Detection in Host-Based Logs0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection0
NLP Based Anomaly Detection for Categorical Time Series0
Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks0
Feature anomaly detection system (FADS) for intelligent manufacturing0
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection AlgorithmsCode1
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations0
Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT HardwareCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified