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
Hybrid Models for Open Set Recognition—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
Open Set Recognition with Conditional Probabilistic Generative Models—0
Open-Set Recognition with Gaussian Mixture Variational Autoencoders—0
Open-Set Recognition with Gradient-Based Representations—0
Open-Set RF Fingerprinting via Improved Prototype Learning—0
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning—0
Open-World Object Detection via Discriminative Class Prototype Learning—0
Show:102550
← PrevPage 14 of 27Next →

No leaderboard results yet.