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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 211–220 of 267 papers

TitleStatusHype
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Open Set Recognition with Conditional Probabilistic Generative Models—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Deep Active Learning via Open Set RecognitionCode0
MMF: A loss extension for feature learning in open set recognitionCode0
More Information Supervised Probabilistic Deep Face Embedding Learning—0
Open-Set Recognition with Gaussian Mixture Variational Autoencoders—0
Generative-Discriminative Feature Representations for Open-Set Recognition—0
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