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

Showing 36013625 of 4856 papers

TitleStatusHype
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation0
World Models for Anomaly Detection during Model-Based Reinforcement Learning Inference0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing0
WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning0
XAI-guided Insulator Anomaly Detection for Imbalanced Datasets0
X-MAN: Explaining multiple sources of anomalies in video0
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection0
You May Need both Good-GAN and Bad-GAN for Anomaly Detection0
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions0
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models0
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things0
0/1 Deep Neural Networks via Block Coordinate Descent0
Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid0
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes0
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET0
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle0
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System0
Leveraging Registers in Vision Transformers for Robust Adaptation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified