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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 2051–2075 of 4856 papers

TitleStatusHype
Anomaly Detection with Generative Adversarial Networks—0
Feature Extraction for Novelty Detection in Network Traffic—0
Functional Anomaly Detection: a Benchmark Study—0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering—0
GAN-based Anomaly Detection in Imbalance Problems—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS—0
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces—0
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management—0
A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams—0
Anomaly Detection with Ensemble of Encoder and Decoder—0
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping—0
COVID-19 Detection Using CT Image Based On YOLOv5 Network—0
Anomaly Detection with Domain Adaptation—0
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection—0
From Light to Rich ERE: Annotation of Entities, Relations, and Events—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection—0
A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves—0
Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection—0
A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact—0
A Multi-Scale A Contrario method for Unsupervised Image 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