Unsupervised Anomaly Detection
169 papers with code • 15 benchmarks • 23 datasets
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.
Benchmarks
These leaderboards are used to track progress in Unsupervised Anomaly Detection
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Libraries
Use these libraries to find Unsupervised Anomaly Detection models and implementationsDatasets
MNIST
Fashion-MNIST
STL-10
Caltech-101
MVTecAD
Reuters-21578
MVTec LOCO AD
KolektorSDD
Subtasks
Latest papers
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
ravih18/uad_evaluation_framework
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Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity.
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
ci-ber/ra
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Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies.
Distillation-based fabric anomaly detection
Given the extensive variability in colors, textures, and defect types, fabric defect detection poses a complex and challenging problem in the field of patterned textures inspection.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
shirowalker/ucad
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Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
Label-Free Multivariate Time Series Anomaly Detection
zqhang/mtgflow
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In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
zhangzjn/ader
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Following this spirit, this paper explores plain ViT architecture for MUAD.
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
finnbehrendt/conditioned-diffusion-models-uad
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Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI.
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions.
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
konsotirop/adamm
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Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances?
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
ruiyinglu/hvq-trans
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NeurIPS 2023
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
