SOTAVerified

Generalized Zero-Shot Learning

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 41–50 of 161 papers

TitleStatusHype
Bias-Awareness for Zero-Shot Learning the Seen and UnseenCode0
Dual Expert Distillation Network for Generalized Zero-Shot LearningCode0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
Generative Dual Adversarial Network for Generalized Zero-shot LearningCode0
Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning(A-CZSL)Code0
A Meta-Learning Framework for Generalized Zero-Shot LearningCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Attribute-Aware Representation Rectification for Generalized Zero-Shot LearningCode0
Data-Free Generalized Zero-Shot LearningCode0
Show:102550
← PrevPage 5 of 17Next →

No leaderboard results yet.