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 2651–2675 of 4856 papers

TitleStatusHype
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization—0
Bottom-up approaches for multi-person pose estimation and it's applications: A brief review—0
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary—0
Bounded Fuzzy Possibilistic Method—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Brain Abnormality Detection by Deep Convolutional Neural Network—0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Brain Structure Ages -- A new biomarker for multi-disease classification—0
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients—0
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps—0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection—0
Bridging Machine Learning and Sciences: Opportunities and Challenges—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
Brittle Features May Help Anomaly Detection—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Building Machine Learning Challenges for Anomaly Detection in Science—0
Burnt area extraction from high-resolution satellite images based on anomaly detection—0
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data—0
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Caformer: Rethinking Time Series Analysis from Causal Perspective—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