Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a v…
Anomaly DetectionAnomaly SegmentationContinual LearningMulti-class Classification+2An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm
One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classific…
ClassificationGeneral ClassificationMulti-class ClassificationOne-Class Classification+1A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed int…
Anomaly DetectionOne-Class ClassificationIdentification of Abnormal States in Videos of Ants Undergoing Social Phase Change
Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-t…
One-Class ClassificationTime Series AnalysisMeta Learning for Few-Shot One-class Classification
We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classific…
ClassificationGeneral ClassificationMeta-LearningOne-Class ClassificationSTEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems
In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their secu…
One-Class Classification$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space
The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space one-class classification principle, we present a multiple kernel learnin…
General ClassificationOne-Class ClassificationUnsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer
Anomaly Detectors are trained on healthy operating condition data and raise an alarm when the measured samples deviate from the training data distribution. This means that the samples used to train the model should be su…
Anomaly DetectionClusteringDimensionality ReductionDomain Adaptation+3Quantum One-class Classification With a Distance-based Classifier
The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of ava…
BIG-bench Machine LearningClassificationGeneral ClassificationOne-Class Classification+2Few-Shot One-Class Classification via Meta-Learning
Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work a…
Anomaly DetectionClassificationFew-Shot LearningGeneral Classification+4Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the neg…
Anomaly DetectionAstronomyGeneral Classificationimbalanced classification+2Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, findi…
Anomaly DetectionClassificationGeneral ClassificationOne-Class Classification+1G2D: Generate to Detect Anomaly
In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples. The…
Anomaly DetectionBinary ClassificationOne-Class ClassificationUse of in-the-wild images for anomaly detection in face anti-spoofing
The traditional approach to face anti-spoofing sees it as a binary classification problem, and binary classifiers are trained and validated on specialized anti-spoofing databases. One of the drawbacks of this approach is…
Anomaly DetectionBinary ClassificationFace Anti-SpoofingOne-Class ClassificationApplying support vector data description for fraud detection
Fraud detection is an important topic that applies to various enterprises such as banking and financial sectors, insurance, government agencies, law enforcement, and more. Fraud attempts have been risen remarkably in cur…
Fraud DetectionOne-Class ClassificationPolicy Entropy for Out-of-Distribution Classification
One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential sa…
BenchmarkingClassificationDeep Reinforcement LearningGeneral Classification+5Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
Machine learning systems and also, specifically, automatic speech recognition (ASR) systems are vulnerable against adversarial attacks, where an attacker maliciously changes the input. In the case of ASR systems, the mos…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)One-Class Classificationspeech-recognition+2Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification
Dictionary learning for sparse representations has been successful in many reconstruction tasks. Simplicial learning is an adaptation of dictionary learning, where subspaces become clipped and acquire arbitrary offsets, …
ClassificationDictionary LearningGeneral ClassificationMulti-class Classification+1Towards Anomaly Detection in Dashcam Videos
Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe followi…
Anomaly DetectionOne-Class ClassificationViral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray …
Anomaly DetectionBinary ClassificationClassificationGeneral Classification+1