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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 121–130 of 267 papers

TitleStatusHype
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Open-set learning with augmented categories by exploiting unlabelled data—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Learning for Transductive Threshold Calibration in Open-World Recognition—0
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
All Beings Are Equal in Open Set Recognition—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
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