Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
One-Class Risk Estimation for One-Class Hyperspectral Image Classification
Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation. However…
ClassificationDeep LearningHyperspectral Image Classificationimage-classification+4Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication
In physical layer authentication (PLA) mechanisms, a verifier decides whether a received message has been transmitted by a legitimate user or an intruder, according to some features of the physical channel over which the…
One-Class ClassificationSelf-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending ev…
Anomaly DetectionEvent DetectionFault DetectionOne-Class ClassificationA One-Class Classification method based on Expanded Non-Convex Hulls
This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the con…
Anomaly DetectionOne-Class ClassificationSupervised Anomaly DetectionCalibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising adv…
Anomaly DetectionOne-Class ClassificationOne-class classifierTime Series+2Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. How…
Anomaly DetectionContrastive LearningOne-Class ClassificationRelationDeep Contrastive One-Class Time Series Anomaly Detection
The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based methods, which reveal only a certain aspect o…
Anomaly DetectionContrastive LearningOne-Class ClassificationTime Series+2Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an unsupervised fashion is very promising. Ho…
Anomaly DetectionOne-Class Classification[Reproducibility Report] Explainable Deep One-Class Classification
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The autho…
Anomaly DetectionClassificationCPUGPU+1Generalized Reference Kernel for One-class Classification
In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities …
ClassificationOne-Class ClassificationOCFormer: One-Class Transformer Network for Image Classification
We propose a novel deep learning framework based on Vision Transformers (ViT) for one-class classification. The core idea is to use zero-centered Gaussian noise as a pseudo-negative class for latent space representation …
Classificationimage-classificationImage ClassificationOne-Class Classification+1"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection
Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is at the forefront of finding new physics beyond the Stan…
Anomaly DetectionMulti-class ClassificationOne-Class ClassificationEarly Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging tech…
ClassificationMyocardial infarction detectionOne-Class ClassificationMulti-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis
Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class d…
One-Class ClassificationOne-class classifierAnomaly Detection in Emails using Machine Learning and Header Information
Anomalies in emails such as phishing and spam present major security risks such as the loss of privacy, money, and brand reputation to both individuals and organizations. Previous studies on email anomaly detection relie…
Anomaly DetectionBIG-bench Machine LearningOne-Class Classification$\ell_p$ Slack Norm Support Vector Data Description
The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere to enclose target objects while linearly …
DescriptiveOne-Class ClassificationImproving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data
When dealing with binary classification of data with only one labeled class data scientists employ two main approaches, namely One-Class (OC) classification and Positive Unlabeled (PU) learning. The former only learns fr…
Binary ClassificationOne-Class ClassificationGenerative Cooperative Learning for Unsupervised Video Anomaly Detection
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less fre…
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionAbuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning
This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. The goal is to discover anomalous and suspicious incidents and scale the investigation efforts by creat…
Abuse DetectionAnomaly DetectionBIG-bench Machine LearningBinary Classification+7Mobile authentication of copy detection patterns
In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applicatio…
Copy DetectionOne-Class Classification