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
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Constructing a meta-learner for unsupervised anomaly detection—0
Coincident Learning for Unsupervised Anomaly Detection—0
IRC-safe Graph Autoencoder for unsupervised anomaly detection—0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation—0
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection—0
Knowledge Distillation for Anomaly Detection—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Label-Free Segmentation of COVID-19 Lesions in Lung CT—0
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection—0
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection—0
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies—0
Learning Citywide Patterns of Life from Trajectory Monitoring—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
Learning Deep Latent Variable Models via Amortized Langevin Dynamics—0
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis—0
Learning Normal Patterns in Musical Loops—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Leveraging 3D Information in Unsupervised Brain MRI Segmentation—0
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET—0
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information—0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction—0
MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images—0
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks—0
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series—0
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary—0
Bias in Unsupervised Anomaly Detection in Brain MRI—0
Unsupervised Anomaly Detection in Parole Hearings using Language Models—0
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised Anomaly Detection—0
Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection—0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Benchmarking Unsupervised Anomaly Detection and Localization—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