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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
Open-Set Recognition of Novel Species in Biodiversity Monitoring—0
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?—0
Open-Set Recognition Using Intra-Class Splitting—0
Open-set Recognition via Augmentation-based Similarity Learning—0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Deep CNN-based Multi-task Learning for Open-Set RecognitionCode0
Accurate Open-set Recognition for Memory WorkloadCode0
SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning ModelsCode0
Vocabulary-informed Zero-shot and Open-set LearningCode0
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