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 2801–2850 of 4856 papers

TitleStatusHype
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT—0
Time Series Anomaly Detection with label-free Model Selection—0
Time Series Data Augmentation for Deep Learning: A Survey—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Time topological analysis of EEG using signature theory—0
TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera—0
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation—0
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping—0
TopoCL: Topological Contrastive Learning for Time Series—0
Topological Data Analysis for Anomaly Detection in Host-Based Logs—0
Topological Obstructions to Autoencoding—0
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference—0
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders—0
Towards AIOps in Edge Computing Environments—0
Towards Anomaly Detection in Dashcam Videos—0
Towards a Theoretical Analysis of PCA for Heteroscedastic Data—0
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging—0
Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions—0
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning—0
Towards Corner Case Detection for Autonomous Driving—0
Towards Cross-domain Few-shot Graph Anomaly Detection—0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization—0
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling—0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
Towards exploring adversarial learning for anomaly detection in complex driving scenes—0
Towards Fair Deep Anomaly Detection—0
Towards Generating Adversarial Examples on Mixed-type Data—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering—0
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach—0
Towards Open Set Video Anomaly Detection—0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization—0
Towards Robust Voice Pathology Detection—0
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression—0
Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine—0
Towards Surveillance Video-and-Language Understanding: New Dataset Baselines and Challenges—0
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation—0
Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
Towards Unsupervised Validation of Anomaly-Detection Models—0
Toward Supervised Anomaly Detection—0
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded 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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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