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 3426–3450 of 4856 papers

TitleStatusHype
An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances—0
An Introduction to Autoencoders—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
An Off-the-shelf Approach to Authorship Attribution—0
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Anomalib: A Deep Learning Library for Anomaly Detection—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Anomalies, Representations, and Self-Supervision—0
Anomalous Change Point Detection Using Probabilistic Predictive Coding—0
Anomalous Client Detection in Federated Learning—0
Anomalous Example Detection in Deep Learning: A Survey—0
AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
Anomaly Awareness—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Abnormal component analysis—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—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