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 2001–2050 of 4856 papers

TitleStatusHype
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly DetectionCode2
FABLE : Fabric Anomaly Detection Automation ProcessCode0
MixedTeacher : Knowledge Distillation for fast inference textural anomaly detectionCode0
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data—0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning—0
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly DetectionCode2
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection—0
Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning—0
Learning on Graphs under Label Noise—0
A Computational Theory and Semi-Supervised Algorithm for ClusteringCode0
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI ForecastingCode0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering—0
Log-based Anomaly Detection based on EVT Theory with feedback—0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation—0
Permutation invariant Gaussian matrix models for financial correlation matrices—0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
A Robust Likelihood Model for Novelty Detection—0
High-dimensional and Permutation Invariant Anomaly DetectionCode0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
ReContrast: Domain-Specific Anomaly Detection via Contrastive ReconstructionCode1
PV Fleet Modeling via Smooth Periodic Gaussian Copula—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System—0
Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder—0
Anomaly Detection Techniques in Smart Grid Systems: A Review—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
UADB: Unsupervised Anomaly Detection BoosterCode1
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedInCode1
A Hybrid Approach for Smart Alert Generation—0
GAD-NR: Graph Anomaly Detection via Neighborhood ReconstructionCode1
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Graph-Level Embedding for Time-Evolving Graphs—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
Anomaly Detection with Variance Stabilized Density EstimationCode0
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion ModelCode1
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion ModelsCode1
Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16—0
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation LearningCode1
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on AnomaliesCode0
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly DetectionCode1
On Diffusion Modeling for Anomaly DetectionCode1
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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