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Open Set Learning

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.

Papers

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Showing 181–190 of 267 papers

TitleStatusHype
Collective Decision of One-vs-Rest Networks for Open Set Recognition—0
Data-Driven Hierarchical Open Set Recognition—0
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
Deep Learning and Open Set Malware Classification: A Survey—0
Deep Learning Approaches for Open Set Wireless Transmitter Authorization—0
Deep Open-Set Recognition for Silicon Wafer Production Monitoring—0
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection—0
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