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

Showing 15511575 of 4856 papers

TitleStatusHype
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing MonitoringCode0
Exploring Deep Anomaly Detection Methods Based on Capsule NetCode0
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly DetectionCode0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly DetectionCode0
Enhancing Interpretability and Generalizability in Extended Isolation ForestsCode0
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural NetworksCode0
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETCode0
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based ModelsCode0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial ManufacturingCode0
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining TasksCode0
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CTCode0
Exact Optimization of Conformal Predictors via Incremental and Decremental LearningCode0
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learningCode0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Evaluating Bayesian Deep Learning Methods for Semantic SegmentationCode0
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offsCode0
Estimate the Implicit Likelihoods of GANs with Application to Anomaly DetectionCode0
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal ImagesCode0
Evaluating Language Models For Threat Detection in IoT Security LogsCode0
Can I trust my anomaly detection system? A case study based on explainable AICode0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style TransferCode0
Enhancing Wrist Fracture Detection with YOLOCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified