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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 161–170 of 267 papers

TitleStatusHype
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition—0
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition—0
All Beings Are Equal in Open Set Recognition—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
A novel network training approach for open set image recognition—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
A Survey on Open-Set Image Recognition—0
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