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 3951–3975 of 4856 papers

TitleStatusHype
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Open-Vocabulary Video Anomaly Detection—0
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing—0
Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection—0
Oracle Analysis of Representations for Deep Open Set Detection—0
OralXrays-9: Towards Hospital-Scale Panoramic X-ray Anomaly Detection via Personalized Multi-Object Query-Aware Mining—0
Outlier Detection by Consistent Data Selection Method—0
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection—0
Outliers resistant image classification by anomaly detection—0
Out-Of-Bag Anomaly Detection—0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models—0
Out-of-Distribution Detection Without Class Labels—0
Overcomplete Frame Thresholding for Acoustic Scene Analysis—0
Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification—0
PAC-Based Formal Verification for Out-of-Distribution Data Detection—0
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness—0
PAC-Wrap: Semi-Supervised PAC Anomaly Detection—0
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
PANDA : Perceptually Aware Neural Detection of Anomalies—0
Paranom: A Parallel Anomaly Dataset Generator—0
Parkinson gait modelling from an anomaly deep representation—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