Papers Supervised Anomaly Detection
“Supervised Anomaly Detection” 태그가 달린 논문 164편 · 필터 해제
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
The deployment of Industrial Anomaly Detection (IAD) in real-world manufacturing frequently encounters a challenging cold-start bottleneck, in which limited normal samples fail to represent the full normal distribution a…
Supervised Anomaly DetectionAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and …
Supervised Anomaly DetectionIntrusion DetectionRethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whet…
Supervised Anomaly DetectionGeneral ClassificationMixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, fai…
Supervised Anomaly DetectionKidney Cancer Detection Using 3D-Based Latent Diffusion Models
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusio…
Supervised Anomaly DetectionCEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection
Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond the training distribution due to decision …
Supervised Anomaly DetectionRASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans
Weakly Supervised Anomaly detection (WSAD) in brain MRI scans is an important challenge useful to obtain quick and accurate detection of brain anomalies when precise pixel-level anomaly annotations are unavailable and on…
Supervised Anomaly DetectionShift Detection and Adaptation for Network Intrusion Detection
Distribution shift, a change in the statistical properties of data over time, poses a critical challenge for deep learning anomaly detection systems. Existing anomaly detection systems often struggle to adapt to these sh…
Supervised Anomaly DetectionNetwork Intrusion DetectionKnowledge DistillationAn Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals
Next generation advanced nuclear reactors are expected to be smaller both in size and power output, relying extensively on fully digital instrumentation and control systems. These reactors will generate a large flow of i…
Supervised Anomaly DetectionBridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies
Anomaly detection (AD) is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal …
Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionFew-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or ex…
Anomaly ClassificationAnomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling t…
Anomaly Detection In Surveillance VideosContrastive LearningMultiple Instance LearningSupervised Anomaly Detection+3Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction
In tackling frequent batch anomalies in high-speed stamping processes, this study introduces a novel semi-supervised in-process anomaly monitoring framework, utilizing accelerometer signals and physics information, to ca…
Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionAutomated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures
Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address thes…
Anomaly DetectionExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Semi-supervised Anomaly Detection+1ISP-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+3A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications
Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikod\'ym theorem, a fundamental concept in measure theory. The key insight from this article …
Anomaly DetectionSupervised Anomaly DetectionTime SeriesUnsupervised 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+2Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs
Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate amount of labeled data and a large volum…
Anomaly DetectionGraph Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionDistribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection
In Open-set Supervised Anomaly Detection (OSAD), the existing methods typically generate pseudo anomalies to compensate for the scarcity of observed anomaly samples, while overlooking critical priors of normal sam…
Anomaly DetectionSupervised Anomaly DetectionBadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection
Image anomaly detection (IAD) is essential in applications such as industrial inspection, medical imaging, and security. Despite the progress achieved with deep learning models like Deep Semi-Supervised Anomaly Detection…
Anomaly DetectionBackdoor AttackDeep LearningSemi-supervised Anomaly Detection+1