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 4151–4200 of 4856 papers

TitleStatusHype
Attribute Restoration Framework for Anomaly DetectionCode0
Latent space conditioning for improved classification and anomaly detection—0
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces—0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
EvAn: Neuromorphic Event-based Anomaly Detection—0
Generalizing Information to the Evolution of Rational Belief—0
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
Deep Anomaly Detection with Deviation NetworksCode0
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion DataCode0
A Promotion Method for Generation Error Based Video Anomaly Detection—0
Attention Guided Anomaly Localization in Images—0
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models—0
Online Adaptive Asymmetric Active Learning with Limited BudgetsCode0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security—0
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
Real-time Anomaly Detection and Classification in Streaming PMU Data—0
Anomaly Detection in Large Scale Networks with Latent Space Models—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Uncertainty on Asynchronous Time Event PredictionCode0
Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor—0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Deep Variational Semi-Supervised Novelty Detection—0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
RAD: On-line Anomaly Detection for Highly Unreliable Data—0
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network—0
Convolutional Neural Network for Multipath Detection in GNSS Receivers—0
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent EmbeddingsCode0
Dynamic Graph Embedding via LSTM History Tracking—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors—0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)—0
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type DataCode0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks—0
Deep Weakly-supervised Anomaly DetectionCode0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
An Ensemble Approach toward Automated Variable Selection for Network Anomaly Detection—0
Intrusion Detection using Sequential Hybrid Model—0
Community-Level Anomaly Detection for Anti-Money Laundering—0
Quick survey of graph-based fraud detection methods—0
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
Deep learning guided Android malware and anomaly detection—0
Abnormal Client Behavior Detection in Federated Learning—0
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis ApproachCode0
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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