Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not wel…
Change DetectionImage-to-Image TranslationOne-Class ClassificationTranslationData refinement for fully unsupervised visual inspection using pre-trained networks
Anomaly detection has recently seen great progress in the field of visual inspection. More specifically, the use of classical outlier detection techniques on features extracted by deep pre-trained neural networks have be…
Anomaly DetectionOne-Class ClassificationOutlier DetectionOn The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
Defect prediction aims at identifying software components that are likely to cause faults before a software is made available to the end-user. To date, this task has been modeled as a two-class classification problem, ho…
One-Class ClassificationPredictionSAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe u…
Novelty DetectionOne-Class ClassificationFROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. Accompli…
One-Class ClassificationOut-of-Distribution DetectionOut of Distribution (OOD) DetectionSelf-Supervised LearningMedical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To addres…
ClassificationDeep Learningimage-classificationImage Classification+2Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images
Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguishing the samples of the minority class fr…
DescriptiveOne-Class ClassificationSelf-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful …
Anomaly DetectionOne-Class ClassificationUBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, a…
Action RecognitionAnomaly DetectionEvent DetectionOne-Class Classification+1Conical Classification For Efficient One-Class Topic Determination
As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classi…
ClassificationOne-Class ClassificationConical Classification For Computationally Efficient One-Class Topic Determination
As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classi…
ClassificationOne-Class ClassificationLearning Not to Reconstruct Anomalies
Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the in…
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionSynthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection
Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only …
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionImpact of Channel Variation on One-Class Learning for Spoof Detection
Margin-based losses, especially one-class classification loss, have improved the generalization capabilities of countermeasure systems (CMs), but their reliability is not tested with spoofing attacks degraded with channe…
ClassificationOne-Class ClassificationDeep Inverse Reinforcement Learning via Adversarial One-Class Classification
Traditional inverse reinforcement learning (IRL) methods require a loop to find the optimal policy for each reward update (called an inner loop), resulting in very time-consuming reward estimation. In contrast, classific…
ClassificationOne-Class Classificationreinforcement-learningReinforcement Learning+1Conceptron: a probabilistic deep one-class classification method
One-class learning through deep architectures is a particularly challenging task; in this scenario the crasis of kernel methods and deep networks can represent a viable strategy to empower already effective methods. In t…
ClassificationOne-Class ClassificationFROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection
Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims mainly due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. A…
One-Class ClassificationOut-of-Distribution DetectionOut of Distribution (OOD) DetectionSelf-Supervised LearningUnsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies
High-quality image digitisation of histological slides is essential for digital pathology to facilitate diagnosis and to develop reliable computer-aided assistance systems. Currently, image quality control (QC) to identi…
Artifact DetectionOne-Class Classificationwhole slide imagesOne-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification
Real-world classification tasks are frequently required to work in an open-set setting. This is especially challenging for few-shot learning problems due to the small sample size for each known category, which prevents e…
ClassificationFew-Shot LearningMeta-LearningOne-Class Classification+1One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior
Recently, semiconductors' demand has exploded in virtual reality, smartphones, wearable devices, the internet of things, robotics, and automobiles. Semiconductor manufacturers want to make semiconductors with high yields…
ClassificationOne-Class Classification