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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 2101–2125 of 4856 papers

TitleStatusHype
Research and application of Transformer based anomaly detection model: A literature review—0
Detecting Anomalous Events in Object-centric Business Processes via Graph Neural NetworksCode0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks—0
Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities—0
Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach—0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Statistical Test for Anomaly Detections by Variational Auto-Encoders—0
IoT Network Traffic Analysis with Deep Learning—0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
One-class anomaly detection through color-to-thermal AI for building envelope inspection—0
Quantum Normalizing Flows for Anomaly Detection—0
Understanding Time Series Anomaly State Detection through One-Class Classification—0
A hybrid IndRNNLSTM approach for real-time anomaly detection in software-defined networks—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
Develop End-to-End Anomaly Detection System—0
Statistical validation of a deep learning algorithm for dental anomaly detection in intraoral radiographs using paired data—0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Retrieval Augmented Deep Anomaly Detection for Tabular DataCode0
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETCode0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology—0
Large Language Model Guided Knowledge Distillation for Time Series 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