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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 241–250 of 267 papers

TitleStatusHype
Large-Scale Long-Tailed Recognition in an Open WorldCode1
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
Open-Set Recognition Using Intra-Class Splitting—0
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition—0
Deep CNN-based Multi-task Learning for Open-Set RecognitionCode0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
Individual common dolphin identification via metric embedding learning—0
Classification-Reconstruction Learning for Open-Set RecognitionCode0
Recent Advances in Open Set Recognition: A Survey—0
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension—0
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