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 1451–1500 of 4856 papers

TitleStatusHype
ONER: Online Experience Replay for Incremental Anomaly Detection—0
Towards Zero-shot 3D Anomaly Localization—0
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—0
SCADE: Scalable Framework for Anomaly Detection in High-Performance System—0
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasetsCode0
State Frequency Estimation for Anomaly Detection—0
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515Code0
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models—0
Deep evolving semi-supervised anomaly detection—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection—0
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey—0
Enhanced anomaly detection in well log data through the application of ensemble GANsCode0
Real-time Anomaly Detection at the L1 Trigger of CMS Experiment—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
Real-Time Anomaly Detection in Video Streams—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering—0
Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches—0
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
Unsupervised Event Outlier Detection in Continuous Time—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation—0
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters—0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial ManufacturingCode0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—0
End-to-End Convolutional Activation Anomaly Analysis for Anomaly Detection—0
Is this Generated Person Existed in Real-world? Fine-grained Detecting and Calibrating Abnormal Human-body—0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Privacy-Preserving Video Anomaly Detection: A Survey—0
Demonstrating the Suitability of Neuromorphic, Event-Based, Dynamic Vision Sensors for In Process Monitoring of Metallic Additive Manufacturing and Welding—0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
Associative Knowledge Graphs for Efficient Sequence Storage and RetrievalCode0
AI Guided Early Screening of Cervical Cancer—0
SADDE: Semi-supervised Anomaly Detection with Dependable ExplanationsCode0
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings—0
INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection—0
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance—0
On-device Anomaly Detection in Conveyor Belt Operations—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