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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 251–260 of 267 papers

TitleStatusHype
Learning the Compositional Spaces for Generalized Zero-shot Learning—0
Distribution Networks for Open Set Learning—0
Query Attack via Opposite-Direction Feature:Towards Robust Image RetrievalCode0
Open Set Learning with Counterfactual Images—0
Collective decision for open set recognition—0
AP18-OLR Challenge: Three Tasks and Their BaselinesCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Recent Advances in Zero-shot Recognition—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Adversarial Robustness: Softmax versus Openmax—0
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