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 3076–3100 of 4856 papers

TitleStatusHype
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's DistanceCode0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving ScenesCode1
Link Analysis meets Ontologies: Are Embeddings the Answer?Code0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection ScoringCode0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model—0
Self-Supervised Predictive Convolutional Attentive Block for Anomaly DetectionCode1
Online Self-Evolving Anomaly Detection in Cloud Computing EnvironmentsCode0
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly DetectionCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
Variation and generality in encoding of syntactic anomaly information in sentence embeddings—0
Through-Foliage Tracking with Airborne Optical Sectioning—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Online-compatible Unsupervised Non-resonant Anomaly DetectionCode0
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security—0
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement—0
A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves—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