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 3326–3350 of 4856 papers

TitleStatusHype
Constrained Adaptive Projection with Pretrained Features for Anomaly DetectionCode0
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objectsCode0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation—0
Auditing Keyword Queries Over Text Documents—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery—0
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenariosCode0
Reconstruction Student with Attention for Student-Teacher Pyramid Matching—0
A novel data-driven algorithm to predict anomalous prescription based on patient's feature set—0
Anomaly Rule Detection in Sequence Data—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
A Taxonomy of Anomalies in Log Data—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Deep Representation Learning with an Information-theoretic Loss—0
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's DistanceCode0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Link Analysis meets Ontologies: Are Embeddings the Answer?Code0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection ScoringCode0
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Online Self-Evolving Anomaly Detection in Cloud Computing EnvironmentsCode0
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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