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
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and LocalizationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
Unsupervised Anomaly Detection in Parole Hearings using Language Models—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Anomaly detection with superexperts under delayed feedbackCode0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization—0
Unsupervised Anomaly Detection on Temporal Multiway Data—0
TadGAN: Time Series Anomaly Detection Using Generative Adversarial NetworksCode2
Label-Free Segmentation of COVID-19 Lesions in Lung CT—0
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Anomaly Detection by Recombining Gated Unsupervised ExpertsCode0
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
Fast Unsupervised Anomaly Detection in Traffic VideosCode1
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportanceCode1
Unsupervised anomaly detection for discrete sequence healthcare data—0
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection—0
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Anomaly Detection in Medical Imaging with Deep Perceptual AutoencodersCode1
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition SoundsCode1
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Probabilistic AutoencoderCode1
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcityCode1
Spoof Face Detection Via Semi-Supervised Adversarial Training—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End OptimizationCode1
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly DetectionCode1
Sub-Image Anomaly Detection with Deep Pyramid CorrespondencesCode1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative StudyCode1
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncodersCode1
Correlation-aware Deep Generative Model for Unsupervised Anomaly DetectionCode1
Iterative energy-based projection on a normal data manifold for anomaly localizationCode1
Unsupervised Anomaly Detection for X-Ray ImagesCode1
Anomaly Detection with Density EstimationCode0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent EmbeddingsCode0
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All datasetsAnoShiftSMAPVehicle ClaimsFashion-MNISTKolektorSDD220NEWSAeBAD-SCaltech-101DAGM2007ECG5000KolektorSDDMNIST

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