Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection
Detection of anomalous events in videos is an important problem in applications such as surveillance. Video anomaly detection (VAD) is well-studied in the one-class classification (OCC) and weakly supervised (WS) setting…
Anomaly DetectionOne-Class ClassificationPseudo LabelVideo Anomaly DetectionDeep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma
Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which p…
Multiple Instance LearningOne-Class Classificationwhole slide imagesEfficient Training of One Class Classification-SVMs
This study examines the use of a highly effective training method to conduct one-class classification. The existence of both positive and negative examples in the training data is necessary to develop an effective classi…
Binary ClassificationClassificationOne-Class ClassificationCredit Card Fraud Detection with Subspace Learning-based One-Class Classification
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial losses. Automating credit card fraud de…
Fraud DetectionOne-Class ClassificationConvolutional autoencoder-based multimodal one-class classification
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable for multimodal data, which relies on two c…
ClassificationDiversityimage-classificationImage Classification+1One-Class Classification for Intrusion Detection on Vehicular Networks
Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning …
ClassificationIntrusion DetectionOne-Class ClassificationNewton Method-based Subspace Support Vector Data Description
In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original data to a subspace optimized for one-cl…
ClassificationOne-Class ClassificationActive anomaly detection based on deep one-class classification
Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples to be labeled by experts and re-trains t…
Active LearningAnomaly DetectionOne-Class ClassificationAn Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it has been a common practice to learn nor…
Anomaly DetectionOne-Class ClassificationA Perceptron-based Fine Approximation Technique for Linear Separation
This paper presents a novel online learning method that aims at finding a separator hyperplane between data points labelled as either positive or negative. Since weights and biases of artificial neurons can directly be r…
One-Class ClassificationCA2: Class-Agnostic Adaptive Feature Adaptation for One-class Classification
One-class classification (OCC), i.e., identifying whether an example belongs to the same distribution as the training data, is essential for deploying machine learning models in the real world. Adapting the pre-trained f…
One-Class ClassificationAnomaly detection with semi-supervised classification based on risk estimators
A significant limitation of one-class classification anomaly detection methods is their reliance on the assumption that unlabeled training data only contains normal instances. To overcome this impractical assumption, we …
Anomaly DetectionOne-Class ClassificationExploring the Optimization Objective of One-Class Classification for Anomaly Detection
One-class classification (OCC) is a longstanding method for anomaly detection. With the powerful representation capability of the pre-trained backbone, OCC methods have witnessed significant performance improvements. Typ…
Anomaly DetectionOne-Class ClassificationTransfer LearningAMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert annotation of anomaly data. However, nearly …
Anomaly DetectionOne-Class ClassificationUnsupervised Anomaly DetectionProtoFL: Unsupervised Federated Learning via Prototypical Distillation
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose signifi…
Federated LearningOne-Class ClassificationOne-class classifierImage Outlier Detection Without Training using RANSAC
Image outlier detection (OD) is an essential tool to ensure the quality of images used in computer vision tasks. Existing algorithms often involve training a model to represent the inlier distribution, and outliers are d…
One-Class ClassificationOutlier DetectionLBL: Logarithmic Barrier Loss Function for One-class Classification
One-class classification (OCC) aims to train a classifier only with the target class data and attracts great attention for its strong applicability in real-world application. Despite a lot of advances have been made in O…
ClassificationOne-Class ClassificationMultimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limi…
2D Human Pose EstimationAnomaly DetectionHuman Pose ForecastingOne-Class Classification+1Restricted Generative Projection for One-Class Classification and Anomaly Detection
We present a simple framework for one-class classification and anomaly detection. The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution. Crucia…
Anomaly DetectionInformativenessOne-Class ClassificationUNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTION
This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG. Existing methods are generally trained in a supervised manner at the classification level, focusing on detecting at …
DeepFake DetectionFace SwappingOne-Class ClassificationOne-class classifier