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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 4701–4750 of 4856 papers

TitleStatusHype
Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy SourcesCode0
PIDForest: Anomaly Detection via Partial IdentificationCode0
Constrained Adaptive Projection with Pretrained Features for Anomaly DetectionCode0
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CTCode0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial ManufacturingCode0
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining TasksCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Contrastive Language Prompting to Ease False Positives in Medical Anomaly DetectionCode0
Evaluating Language Models For Threat Detection in IoT Security LogsCode0
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methodsCode0
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood PlanersCode0
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUICode0
Deep Anomaly Detection for Generalized Face Anti-SpoofingCode0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving SafetyCode0
Simulation-Assisted Decorrelation for Resonant Anomaly DetectionCode0
Simulation Assisted Likelihood-free Anomaly DetectionCode0
Evaluating Bayesian Deep Learning Methods for Semantic SegmentationCode0
Outlier Exposure with Confidence Control for Out-of-Distribution DetectionCode0
Estimate the Implicit Likelihoods of GANs with Application to Anomaly DetectionCode0
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain ShiftCode0
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial VulnerabilityCode0
Weakly-Supervised Anomaly Detection in the Milky WayCode0
PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose TokenizationCode0
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offsCode0
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly DetectionCode0
Ensemble Clustering for Graphs: Comparisons and ApplicationsCode0
Enhancing Wrist Fracture Detection with YOLOCode0
Position Regression for Unsupervised Anomaly DetectionCode0
Single-Model Attribution of Generative Models Through Final-Layer InversionCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style TransferCode0
Enhancing Unsupervised Anomaly Detection with Score-Guided NetworkCode0
Achieving Counterfactual Fairness for Anomaly DetectionCode0
Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC LossCode0
Practical data monitoring in the internet-services domainCode0
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated TransformersCode0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
Continuous online sequence learning with an unsupervised neural network modelCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
Precision and Recall for Time SeriesCode0
Enhancing Robustness of On-line Learning Models on Highly Noisy DataCode0
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly DetectionCode0
Continual Learning Approaches for Anomaly DetectionCode0
Predicting Next Local Appearance for Video Anomaly DetectionCode0
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial VideosCode0
A Three-Stage Anomaly Detection Framework for Traffic VideosCode0
Algorithmic Frameworks for the Detection of High Density AnomaliesCode0
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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