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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 221–230 of 267 papers

TitleStatusHype
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Latent Cognizance: What Machine Really Learns—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
Reconstruction guided Meta-learning for Few Shot Open Set Recognition—0
Learning for Transductive Threshold Calibration in Open-World Recognition—0
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis—0
Learning the Compositional Spaces for Generalized Zero-shot Learning—0
On the link between generative semi-supervised learning and generative open-set recognition—0
LORD: Leveraging Open-Set Recognition with Unknown Data—0
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