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 3901–3950 of 4856 papers

TitleStatusHype
Uncertainty-Aware Deep Classifiers using Generative Models—0
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting—0
LSTM-based Anomaly Detection for Non-linear Dynamical System—0
Anomaly Detection with Domain Adaptation—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—0
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems—0
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images—0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments—0
Anomaly Detection with Tensor Networks—0
Camouflaged Object DetectionCode1
Rethinking Assumptions in Deep Anomaly DetectionCode1
Investigation Into the Viability of Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at the LHC—0
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcityCode1
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly DetectionCode1
Tensor decomposition to Compress Convolutional Layers in Deep LearningCode0
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality DetectionCode0
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks—0
Factor Analysis of Mixed Data for Anomaly Detection—0
SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays—0
Master-Auxiliary: an efficient aggregation strategy for video anomaly detection—0
Tractometry-based Anomaly Detection for Single-subject White Matter Analysis—0
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation—0
A Metric Learning Approach to Anomaly Detection in Video GamesCode0
An Incremental Clustering Method for Anomaly Detection in Flight Data—0
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and MonitoringCode1
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars—0
Anomaly Detection in Cloud Components—0
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic GraphsCode1
A Weighted Mutual k-Nearest Neighbour for Classification MiningCode0
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End OptimizationCode1
Integrated Methodology to Cognitive Network \& Slice Management in Virtualized 5G Networks—0
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches—0
Learning Generalized Spoof Cues for Face Anti-spoofingCode1
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
A Review of Computer Vision Methods in Network Security—0
Sub-Image Anomaly Detection with Deep Pyramid CorrespondencesCode1
Classification-Based Anomaly Detection for General DataCode1
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly DetectionCode1
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkCode0
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible TransformationsCode1
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Optimal Strategies Against Generative AttacksCode0
RaPP: Novelty Detection with Reconstruction along Projection PathwayCode1
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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