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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 2201–2250 of 4856 papers

TitleStatusHype
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT—0
Region and Spatial Aware Anomaly Detection for Fundus Images—0
Fast and Multi-aspect Mining of Complex Time-stamped Event StreamsCode1
Memory Maps for Video Object Detection and Tracking on UAVs—0
Time series anomaly detection with reconstruction-based state-space modelsCode0
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow—0
Achieving Counterfactual Fairness for Anomaly DetectionCode0
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network—0
Early Warning Signals of Social Instabilities in Twitter Data—0
Unsupervised Recycled FPGA Detection Using Symmetry Analysis—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
Multi-Task Self-Supervised Time-Series Representation Learning—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum—0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails—0
Multimodal Industrial Anomaly Detection via Hybrid FusionCode2
Unsupervised Pathology Detection: A Deep Dive Into the State of the ArtCode1
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baselineCode1
Texture-Based Input Feature Selection for Action Recognition—0
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients—0
MDF-Net for abnormality detection by fusing X-rays with clinical dataCode1
Interruptions detection in video conferencesCode0
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical SystemsCode0
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection—0
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
Set Features for Fine-grained Anomaly DetectionCode1
Explainable Contextual Anomaly Detection using Quantile Regression ForestsCode0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Memory-augmented Online Video Anomaly DetectionCode1
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection—0
CNTS: Cooperative Network for Time SeriesCode0
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine—0
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Collaborative Discrepancy Optimization for Reliable Image Anomaly LocalizationCode1
A method for incremental discovery of financial event types based on anomaly detection—0
Zero-Shot Anomaly Detection via Batch NormalizationCode1
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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