Papers Unsupervised Anomaly Detection
“Unsupervised Anomaly Detection” 태그가 달린 논문 627편 · 필터 해제
MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI
Unsupervised anomaly detection (UAD) methods for brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how each anomaly map is aligned with the reference, how and on which data…
Unsupervised Anomaly DetectionStructure-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 DetectionSwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection
Industrial anomaly detection aims to identify and localize defective regions without relying on exhaustive annotations of all possible defect types. Although recent unsupervised methods have achieved strong performance, …
Unsupervised Anomaly DetectionUnsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection m…
Unsupervised Anomaly DetectionLearning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly segmentation remain limited by their reliance on pixel-level …
Unsupervised Anomaly DetectionTest-time AdaptationUnsupervised Memory-Enhanced Video Transformers: Obstacle Detection for Autonomous Agricultural Rover
While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge. Conventional safety sensors, such as LiDAR, fail to detect obstacles positione…
Unsupervised Anomaly DetectionMambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
While recent advancements in anomaly detection have demonstrated the efficacy of CNN- and Transformer-based approaches, these architectures face inherent limitations: CNNs struggle to capture long-range dependencies, whe…
Unsupervised Anomaly DetectionDistinguishing indistinguishable attractors: Unsupervised anomaly detection with reservoir computers
Detecting when a nonlinear dynamical system departs from its normal regime is a recurring problem across the sciences, from cardiology to climate and energy systems. We show that a very simple Kolmogorov--Smirnov test on…
Unsupervised Anomaly DetectionBeer-Lambert Guided Representation Learning for Unsupervised Anomaly Detection in Sub-THz Food Inspection Images
Food manufacturing requires reliable inspection systems to detect foreign material contamination and maintain product safety. Sub-THz transmission imaging provides material-dependent attenuation characteristics that are …
Unsupervised Anomaly DetectionRepresentation LearningData Collection for Training Quality-Control AI in Carpet Manufacturing
Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at the line speeds and widths of modern looms. We present a design propo…
Unsupervised Anomaly DetectionA Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, ti…
Unsupervised Anomaly DetectionAnomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a …
Unsupervised Anomaly DetectionDiffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion …
Unsupervised Anomaly DetectionFeature EngineeringDual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to increasingly complex and heterogeneous norma…
Unsupervised Anomaly DetectionMulti-class Anomaly DetectionCatching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning
Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learni…
Unsupervised Anomaly Detection