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 876–900 of 4856 papers

TitleStatusHype
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Atom dimension adaptation for infinite set dictionary learning—0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development—0
GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning—0
Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning—0
Deep Learning for Video Anomaly Detection: A Review—0
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection—0
GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines—0
Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection—0
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures—0
Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study—0
The role of data embedding in quantum autoencoders for improved anomaly detectionCode0
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection—0
Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection—0
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks—0
Oddballness: universal anomaly detection with language models—0
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data AnalysisCode0
NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks—0
Activity-Guided Industrial Anomalous Sound Detection against Interferences—0
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model—0
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations—0
Interpreting Outliers in Time Series Data through Decoding Autoencoder—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