Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially lab…
Anomaly DetectionClassificationOne-Class ClassificationOne-class classifier+2Deep Visual Anomaly detection with Negative Learning
With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly detection, improvements in deep learning ope…
Anomaly DetectionHallucinationOne-Class ClassificationDOC3-Deep One Class Classification using Contradictions
This paper introduces the notion of learning from contradictions (a.k.a Universum learning) for deep one class classification problems. We formalize this notion for the widely adopted one class large-margin loss, and pro…
Anomaly DetectionClassificationInductive LearningOne-Class ClassificationGraph-Embedded Subspace Support Vector Data Description
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-cla…
General ClassificationGraph EmbeddingOne-Class ClassificationMulti-view Deep One-class Classification: A Systematic Exploration
One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to realms like anomaly detection. As modern soc…
Anomaly DetectionClassificationGeneral ClassificationOne-Class ClassificationFederated Learning-based Active Authentication on Mobile Devices
User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. Due to lack of negative class data, it is often modeled as a one-class…
Federated LearningOne-Class ClassificationOne-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, wh…
EEGElectroencephalogram (EEG)Event DetectionOne-Class ClassificationMeta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for super…
Anomaly DetectionFew-Shot LearningMeta-LearningOne-Class Classification+1Optimised one-class classification performance
We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NND), Localised Nearest Neighbour Distance…
ClassificationGeneral ClassificationOne-Class ClassificationAverage Localised Proximity: A new data descriptor with good default one-class classification performance
One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive examples of that class, and no counter-example…
ClassificationGeneral ClassificationOne-Class ClassificationDeep One-Class Classification via Interpolated Gaussian Descriptor
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models…
Anomaly DetectionClassificationOne-Class ClassificationUnsupervised Anomaly DetectionA Joint Representation Learning and Feature Modeling Approach for One-class Recognition
One-class recognition is traditionally approached either as a representation learning problem or a feature modeling problem. In this work, we argue that both of these approaches have their own limitations; and a more eff…
General ClassificationOne-Class ClassificationRepresentation LearningOne-Class Classification: A Survey
One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class. The goal of OCC is to learn a representation and/or a classifier that e…
ClassificationGeneral ClassificationMulti-class ClassificationOne-Class Classification+1One-class Classification Robust to Geometric Transformation
Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images. However, they cannot i…
ClassificationGeneral ClassificationOne-Class ClassificationOne-class classifierTowards Fair Deep Anomaly Detection
Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science. Recently, deep anomaly detection methods were shown to achieve superior results particularly in complex d…
Anomaly DetectionFairnessOne-Class ClassificationMOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection
Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to incomplete knowledge about the data distribution or an unknown process that suddenly comes into play and distorts observations.…
Anomaly DetectionClassificationGeneral ClassificationOne-Class ClassificationTimeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Real-world timeseries have complex underlying temporal dynamics and the detection of anomalies is challenging. In this paper, we propose the Temporal Hierarchical One-Class (THOC) network, a temporal one-class classifica…
Anomaly DetectionClusteringOne-Class ClassificationRepresentation LearningFROCC: Fast Random projection-based One-Class Classification
We present Fast Random projection-based One-Class Classification (FROCC), an extremely efficient method for one-class classification. Our method is based on a simple idea of transforming the training data by projecting i…
ClassificationGeneral ClassificationOne-Class ClassificationMinimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification
One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel e…
General ClassificationOne-Class ClassificationLearning and Evaluating Representations for Deep One-class Classification
We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not o…
Anomaly DetectionClassificationContrastive LearningData Augmentation+5