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 976–1000 of 4856 papers

TitleStatusHype
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
MAMBO: High-Resolution Generative Approach for Mammography Images—0
Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures—0
MLOps with Microservices: A Case Study on the Maritime Domain—0
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs—0
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline—0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems—0
An AI-Based Public Health Data Monitoring System—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric RepresentationCode0
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded DevicesCode0
Generator Based Inference (GBI)Code0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCCode0
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems—0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach—0
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning—0
Is Hyperbolic Space All You Need for Medical Anomaly Detection?—0
Fog Intelligence for Network 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