Papers Self-Supervised Anomaly Detection
“Self-Supervised Anomaly Detection” 태그가 달린 논문 33편 · 필터 해제
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central chall…
Self-Supervised Anomaly DetectionTime Series Anomaly DetectionSelf-Supervised LearningLayer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning
Modern multimodal systems deployed in industrial and safety-critical environments must remain reliable under partial sensor failures, signal degradation, or cross-modal inconsistencies. This work introduces a mathematica…
Self-Supervised Anomaly DetectionRepresentation LearningDemographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities in diagnostic performance. These limitati…
Self-Supervised Anomaly DetectionRepresentation LearningECG ClassificationVariational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms
Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform characteristics. This study reframes P-wa…
Self-Supervised Anomaly DetectionISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets tha…
Anomaly DetectionDefect DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly Detection+3Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta lear…
Anomaly DetectionMeta-LearningSelf-Supervised Anomaly DetectionSAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
The proliferation of IoT devices has significantly increased network vulnerabilities, creating an urgent need for effective Intrusion Detection Systems (IDS). Machine Learning-based IDS (ML-IDS) offer advanced detection …
Anomaly DetectionIntrusion DetectionNetwork Intrusion DetectionSelf-Supervised Anomaly Detection+2Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods
Accurate anomaly detection is critical in vision-based infrastructure inspection, where it helps prevent costly failures and enhances safety. Self-Supervised Learning (SSL) offers a promising approach by learning robust …
Anomaly DetectionSelf-Supervised Anomaly DetectionSelf-Supervised LearningSupervised Anomaly DetectionSelf-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis
Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets. This study introduces a novel approach using self-superv…
Anomaly DetectionDiagnosticSelf-Supervised Anomaly DetectionSpecificity+1Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data r…
Anomaly DetectionDefect DetectionSelf-Supervised Anomaly DetectionAn AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solutions are trained to emulate these imperfect…
Anomaly DetectionDenoisingDiagnosticPseudo Label+3Anomaly Detection by Context Contrasting
Anomaly detection focuses on identifying samples that deviate from the norm. When working with high-dimensional data such as images, a crucial requirement for detecting anomalous patterns is learning lower-dimensional re…
Anomaly DetectionSelf-Supervised Anomaly DetectionSelf-Supervised LearningSupervised Anomaly DetectionAnomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite advancements, existing systems often miss ra…
Anomaly DetectionAnomaly LocalizationDiagnosticSelf-Supervised Anomaly Detection+3MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network
Control Area Network (CAN) is an essential communication protocol that interacts between Electronic Control Units (ECUs) in the vehicular network. However, CAN is facing stringent security challenges due to innate securi…
Anomaly DetectionIntrusion DetectionKnowledge DistillationSelf-Supervised Anomaly Detection+1LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs
System logs are some of the most important information for the maintenance of software systems, which have become larger and more complex in recent years. The goal of log-based anomaly detection is to automatically detec…
Anomaly DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly DetectionHyperbolic Anomaly Detection
Anomaly detection is a challenging computer vision task in industrial scenario. Advancements in deep learning constantly revolutionize vision-based anomaly detection methods and considerable progress has been made in…
Anomaly DetectionBenchmarkingSelf-Supervised Anomaly DetectionSupervised Anomaly DetectionCL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection
In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, t…
Anomaly DetectionContrastive LearningSelf-Supervised Anomaly DetectionSupervised Anomaly Detection+1Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy
Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assump…
Anomaly DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly DetectionUnsupervised Anomaly DetectionSeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets
We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises of acquired and derived heterogeneous data modalities th…
Anomaly ClassificationAnomaly DetectionData AugmentationRecommendation Systems+2End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection
Self-supervised learning (SSL) has emerged as a promising paradigm that presents self-generated supervisory signals to real-world problems, bypassing the extensive manual labeling burden. SSL is especially attractive for…
Anomaly DetectionData AugmentationSelf-Supervised Anomaly DetectionSelf-Supervised Learning+1