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 3451–3500 of 4856 papers

TitleStatusHype
Smart Vectorizations for Single and Multiparameter PersistenceCode0
MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detectionCode1
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps—0
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoTCode0
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
Concentration bounds for the empirical angular measure with statistical learning applicationsCode0
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite RankingCode0
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection—0
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection—0
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses—0
Isconna: Streaming Anomaly Detection with Frequency and PatternsCode0
MIST: Multiple Instance Self-Training Framework for Video Anomaly DetectionCode1
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
Online Forecasting and Anomaly Detection Based on the ARIMA ModelCode1
Detecting Anomalies Through Contrast in Heterogeneous Data—0
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies—0
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection—0
Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoderCode1
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection—0
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesCode1
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Anomaly Detection Under Multiplicative Noise Model Uncertainty—0
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty DetectionCode0
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation—0
Learning Stable Representations with Full Encoder—0
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems—0
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI—0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability—0
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving NoiseCode0
Anomaly Detection using Principles of Human PerceptionCode0
A Deep Learning Approach for Active Anomaly Detection of Extragalactic TransientsCode0
Unsupervised Two-Stage Anomaly Detection—0
SSD: A Unified Framework for Self-Supervised Outlier DetectionCode1
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
Enhancing Robustness of On-line Learning Models on Highly Noisy DataCode0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering—0
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
Unsupervised anomaly detection in digital pathology using GANs—0
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
Bump Hunting in Latent SpaceCode0
Learning-Based Vulnerability Analysis of Cyber-Physical Systems—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