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 4076–4100 of 4856 papers

TitleStatusHype
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
Deep Nearest Neighbor Anomaly Detection—0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
A flexible outlier detector based on a topology given by graph communities—0
Taurus: A Data Plane Architecture for Per-Packet ML—0
A review on outlier/anomaly detection in time series data—0
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data—0
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Memory Augmented Generative Adversarial Networks for Anomaly Detection—0
movie2trailer: Unsupervised trailer generation using Anomaly detection—0
Anomaly Detection by One Class Latent Regularized Networks—0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems—0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Learning a distance function with a Siamese network to localize anomalies in videos—0
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
Universal Data Anomaly Detection via Inverse Generative Adversary Network—0
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting—0
An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework—0
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning—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