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 4601–4625 of 4856 papers

TitleStatusHype
Forensic Video Analytic Software—0
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
FortuneTeller: Predicting Microarchitectural Attacks via Unsupervised Deep Learning—0
Foundation Models for Anomaly Detection: Vision and Challenges—0
Foundation Models for Time Series: A Survey—0
Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Frames and vertex-frequency representations in graph fractional Fourier domain—0
Framing Algorithmic Recourse for Anomaly Detection—0
Quick survey of graph-based fraud detection methods—0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection—0
Free-riders in Federated Learning: Attacks and Defenses—0
Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows—0
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance—0
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection—0
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping—0
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection—0
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management—0
From Light to Rich ERE: Annotation of Entities, Relations, and Events—0
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Learning Stable Representations with Full Encoder—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