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 4001–4025 of 4856 papers

TitleStatusHype
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
A Weighted Mutual k-Nearest Neighbour for Classification MiningCode0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Integrated Methodology to Cognitive Network \& Slice Management in Virtualized 5G Networks—0
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches—0
A Review of Computer Vision Methods in Network Security—0
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkCode0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame InductionCode0
Optimal Strategies Against Generative AttacksCode0
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem—0
Learning Compliance Adaptation in Contact-Rich Manipulation—0
The 4th AI City Challenge—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study—0
A Kernel Two-sample Test for Dynamical Systems—0
Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing—0
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection—0
Sequential Anomaly Detection using Inverse Reinforcement Learning—0
Network Anomaly Detection based on Tensor Decomposition—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