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 251–300 of 506 papers

TitleStatusHype
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection—0
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point CloudsCode0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human DiseaseCode1
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
RUAD: unsupervised anomaly detection in HPC systems—0
Unsupervised Anomaly Localization with Structural Feature-AutoencodersCode1
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical ProcessesCode1
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CTCode0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization—0
Detecting Multivariate Time Series Anomalies with Zero Known LabelCode1
DSR -- A dual subspace re-projection network for surface anomaly detectionCode1
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space—0
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
Learning Citywide Patterns of Life from Trajectory Monitoring—0
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and SegmentationCode1
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching—0
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomographyCode0
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection—0
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
A Unified Model for Multi-class Anomaly DetectionCode2
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
Benchmarking Unsupervised Anomaly Detection and Localization—0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Attention-based residual autoencoder for video anomaly detectionCode1
PAC-Wrap: Semi-Supervised PAC Anomaly Detection—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest RadiographsCode1
IRC-safe Graph Autoencoder for unsupervised anomaly detection—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection AlgorithmsCode1
AI for human assessment: What do professional assessors need?—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly DetectionCode0
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked AutoencoderCode0
AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-DecoderCode1
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
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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