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 1301–1325 of 4856 papers

TitleStatusHype
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping—0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making—0
TopoCL: Topological Contrastive Learning for Time Series—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
LAST SToP For Modeling Asynchronous Time Series—0
A Poisson Process AutoDecoder for X-ray Sources—0
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
Using Causality for Enhanced Prediction of Web Traffic Time Series—0
Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework—0
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics ExperimentsCode0
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus—0
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes—0
Battery State of Health Estimation Using LLM Framework—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
si4onnx: A Python package for Selective Inference in Deep Learning Models—0
Detecting Anomalies Using Rotated Isolation Forest—0
KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units—0
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis—0
MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction—0
LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—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