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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 4856 papers

TitleStatusHype
Foundation Models for Time Series: A Survey—0
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly DetectionCode0
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System—0
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly DetectionCode1
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly DetectionCode1
VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection—0
Beyond Conventional Transformers: The Medical X-ray Attention (MXA) Block for Improved Multi-Label Diagnosis Using Knowledge DistillationCode0
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection—0
ZClip: Adaptive Spike Mitigation for LLM Pre-TrainingCode2
Analytical Discovery of Manifold with Machine Learning—0
Improving log-based anomaly detection through learned adaptive filter—0
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection—0
What is AI, what is it not, how we use it in physics and how it impacts... you—0
Anomaly Detection for Hybrid Butterfly Subspecies via Probability FilteringCode0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series ForecastingCode0
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated TransformersCode0
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks—0
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks—0
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip StatisticsCode0
Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly DetectionCode0
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous DomainsCode0
A Dataset for Semantic Segmentation in the Presence of Unknowns—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