SOTAVerified

Unsupervised Anomaly Detection

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.

Source: Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Papers

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Showing 401–450 of 506 papers

TitleStatusHype
Anomaly Detection Based on Isolation Mechanisms: A Survey—0
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators—0
Unsupervised Deep Learning for IoT Time Series—0
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models—0
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
Anomalib: A Deep Learning Library for Anomaly Detection—0
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation—0
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection—0
Toward Supervised Anomaly Detection—0
AI for human assessment: What do professional assessors need?—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Unsupervised Recycled FPGA Detection Using Symmetry Analysis—0
A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—0
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection—0
UaiNets: From Unsupervised to Active Deep Anomaly Detection—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving—0
A2Log: Attentive Augmented Log Anomaly Detection—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI—0
DFM: Interpolant-free Dual Flow Matching—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACR-NTL (zero-shot, test anomaly ratio=1%)ROC-AUC FAR62.5—Unverified
2ACR-DSVDD (zero-shot, anomaly ratio=1%)ROC-AUC FAR62—Unverified
3ACR-NTL (zero-shot, test anomaly ratio=20%)ROC-AUC FAR62—Unverified
4ACR-DSVDD (zero-shot, anomaly ratio=20%)ROC-AUC FAR59.1—Unverified
5COPODROC-AUC FAR50.42—Unverified
6OC-SVMROC-AUC FAR49.57—Unverified
7SO-GAALROC-AUC FAR49.35—Unverified
8ECOD Li et al. (2022)ROC-AUC FAR49.19—Unverified
9LOFROC-AUC FAR34.96—Unverified
10deepSVDDROC-AUC FAR34.53—Unverified
#ModelMetricClaimedVerifiedStatus
1DFM (flow matching)F194.1—Unverified
2ContextFlow++ (Glow-based)F193.62—Unverified
3TranAdF189.15—Unverified
4MTAD-GATF188.8—Unverified
5CAE-MF188.27—Unverified
6OmniAnomalyF187.28—Unverified
7GlowF186.05—Unverified
8GDNF185.18—Unverified
9USADF181.86—Unverified
#ModelMetricClaimedVerifiedStatus
1SOMAUC65.43—Unverified
2Isolation ForestAUC59.42—Unverified
3Latent Outlier ExposureAUC58.59—Unverified
4NeuTraL-ADAUC57.03—Unverified
5RSRAEAUC55.38—Unverified
6SOM-DAGMMAUC53.82—Unverified
7Local Outlier FactorAUC52.86—Unverified
8One Class Support Vector MachinesAUC51.68—Unverified
9DAGMMAUC51.22—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC-ROC0.85—Unverified
2RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
3RSRAEAUC-ROC0.75—Unverified
4RSRAEAUC-ROC0.69—Unverified
5RSRAEAUC-ROC0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC98.1—Unverified
2WeakREST-UnSegmentation AP76.9—Unverified
3DSRSegmentation AP61.4—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1MSFRDetection AUROC87.1—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffusionADDetection AUROC99.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC96—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUROC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1TranADPrecision92.62—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1P-CAE W-MSE (Tilted View)AUROC78.1—Unverified