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 3626–3650 of 4856 papers

TitleStatusHype
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection—0
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection—0
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing—0
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning—0
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework—0
LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience—0
INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection—0
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems—0
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling—0
Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study—0
Local Anomaly Detection in Videos using Object-Centric Adversarial Learning—0
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection—0
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection—0
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems—0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
Log-based Anomaly Detection of Enterprise Software: An Empirical Study—0
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs—0
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks—0
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection—0
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction—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