Unsupervised Anomaly Detection
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Benchmarks
AnoShift
SMAP
Vehicle Claims
KolektorSDD2
20NEWS
AeBAD-S
Caltech-101
DAGM2007
ECG5000
Fashion-MNIST
KolektorSDD
MNIST
PRONTO
Reuters-21578
SMD
STL-10
Synthetic
TIMo
Most implemented
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Glow: Generative Flow with Invertible 1x1 Convolutions
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
Towards Total Recall in Industrial Anomaly Detection
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Papers
Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding
Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-rea…
Unsupervised Anomaly DetectionGenerative multi-domain transfer learning for fault detection in data-scarce wind turbines
Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require sufficient fault-free training data to learn the normal operation beha…
Unsupervised Anomaly DetectionTransfer LearningUnsupervised Anomaly Detection Using Flow Matching on Tabular Data
Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training. Such training-set contamination violates the clean-normal data assumption unde…
Unsupervised Anomaly DetectionUnsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for high-risk medical AI, scalable automate…
Unsupervised Anomaly Detection3D ReconstructionGroup Equivariant Diffusion for Anomaly Detection in Computational Cytology
Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be trained on normal slide-negative patches a…
Unsupervised Anomaly DetectionMultiple Instance LearningXMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection
The remarkable success of reconstruction-based methods in Unsupervised Anomaly Detection (UAD) lies in their ability to identify and localize anomalies by modeling discrepancies between input images and their reconstruct…
Unsupervised Anomaly DetectionMulti-class Anomaly Detection