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 1001–1050 of 4856 papers

TitleStatusHype
Meta-survey on outlier and anomaly detectionCode0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Learning normal asymmetry representations for homologous brain structuresCode0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
Leveraging Log Instructions in Log-based Anomaly DetectionCode0
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound DetectionCode0
Learning Deep Features for One-Class ClassificationCode0
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image AnalysisCode0
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity MappingCode0
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly DetectionCode0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
Abnormal Event Detection in Videos using Spatiotemporal AutoencoderCode0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context LearningCode0
Language-Assisted Feature Transformation for Anomaly DetectionCode0
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric DataCode0
Latent Space Autoregression for Novelty DetectionCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
Kernel-Based Anomaly Detection Using Generalized Hyperbolic ProcessesCode0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
Kinematic Detection of Anomalies in Human Trajectory DataCode0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly DetectionCode0
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded DevicesCode0
Known Unknowns: Uncertainty Quality in Bayesian Neural NetworksCode0
Is AUC the best measure for practical comparison of anomaly detectors?Code0
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical DomainCode0
Isconna: Streaming Anomaly Detection with Frequency and PatternsCode0
A Three-Stage Anomaly Detection Framework for Traffic VideosCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Attribute Restoration Framework for Anomaly DetectionCode0
Invertible Neural Networks for Graph PredictionCode0
ATD: Anomalous Topic Discovery in High Dimensional Discrete DataCode0
Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough PatentsCode0
Inverting Adversarially Robust Networks for Image SynthesisCode0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory ModelsCode0
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series ForecastingCode0
Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly DetectionCode0
Input complexity and out-of-distribution detection with likelihood-based generative modelsCode0
InQMAD: Incremental Quantum Measurement Anomaly DetectionCode0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
Inferring Networks From Random Walk-Based Node SimilaritiesCode0
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained FeatureCode0
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly DetectionCode0
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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