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 451–475 of 4856 papers

TitleStatusHype
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
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Battery State of Health Estimation Using LLM Framework—0
KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units—0
si4onnx: A Python package for Selective Inference in Deep Learning Models—0
Detecting Anomalies Using Rotated Isolation Forest—0
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect CommunicationCode0
MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—0
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis—0
LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience—0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL ChallengeCode0
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?—0
Mitigating Spurious Negative Pairs for Robust Industrial Anomaly DetectionCode0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Efficient Client Selection in Federated Learning—0
Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector—0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Median of Forests for Robust Density Estimation—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