SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 171–180 of 267 papers

TitleStatusHype
Taking Class Imbalance Into Account in Open Set Recognition Evaluation—0
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition—0
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension—0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
Toward an Efficient Multi-class Classification in an Open Universe—0
Towards Accurate Open-Set Recognition via Background-Class Regularization—0
Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning—0
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification—0
Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation—0
Unlocking Transfer Learning for Open-World Few-Shot Recognition—0
Show:102550
← PrevPage 18 of 27Next →

No leaderboard results yet.