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 131–140 of 267 papers

TitleStatusHype
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios—0
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition—0
Semi-supervised Vocabulary-informed Learning—0
Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)—0
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition—0
Specialized Support Vector Machines for Open-set Recognition—0
Structure-based Anomaly Detection and Clustering—0
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Show:102550
← PrevPage 14 of 27Next →

No leaderboard results yet.