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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 231–240 of 267 papers

TitleStatusHype
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Face Detection on Surveillance Images—0
Open Set Medical Diagnosis—0
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?—0
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning—0
Primate Face Identification in the Wild—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set RecognitionCode0
P-ODN: Prototype based Open Deep Network for Open Set Recognition—0
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition—0
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