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 201–250 of 506 papers

TitleStatusHype
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on TextCode0
Distillation-based fabric anomaly detectionCode0
Anomalib: A Deep Learning Library for Anomaly DetectionCode0
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and PracticeCode0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly DetectionCode0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss AmplificationCode0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data ContaminationCode0
Position Regression for Unsupervised Anomaly DetectionCode0
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly DetectionCode0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
Concentration bounds for the empirical angular measure with statistical learning applicationsCode0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRICode0
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly DetectionCode0
Learning Neural Representations for Network Anomaly DetectionCode0
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated ThresholdingCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomaliesCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly DetectionCode0
Deep Anomaly Detection on Tennessee Eastman Process Data—0
Deep Anomaly Detection in Text—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Incorporating Privileged Information to Unsupervised Anomaly Detection—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
An optimization method for out-of-distribution anomaly detection models—0
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection—0
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised 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