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 3701–3750 of 4856 papers

TitleStatusHype
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data—0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning—0
Anomaly Detection in a Large-scale Cloud Platform—0
An Empirical Investigation of Contextualized Number PredictionCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Action Sequence Augmentation for Early Graph-based Anomaly DetectionCode0
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning—0
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression—0
Reconstruction by Inpainting for Visual Anomaly DetectionCode1
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features—0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—0
Detecting Anomalies from Video-Sequences: a Novel Descriptor—0
Anomaly Detection With Conditional Variational Autoencoders—0
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm RateCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
Algorithmic Frameworks for the Detection of High Density AnomaliesCode0
Unsupervised 3D Brain Anomaly Detection—0
Anomaly detection with superexperts under delayed feedbackCode0
Anomaly Detection in Large Labeled Multi-Graph Databases—0
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect—0
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data—0
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-rayCode0
Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-raysCode0
OneFlow: One-class flow for anomaly detection based on a minimal volume region—0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining—0
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml—0
Deep Anomaly Detection by Residual Adaptation—0
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting—0
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series—0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
A comparison of classical and variational autoencoders for anomaly detection—0
A Survey on Deep Learning Techniques for Video Anomaly Detection—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
STAN: Synthetic Network Traffic Generation with Generative Neural ModelsCode0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization—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