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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

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Showing 121–130 of 161 papers

TitleStatusHype
Relation-based Generalized Zero-shot Classification with the Domain Discriminator on the shared representation—0
Alleviating Feature Confusion for Generative Zero-shot LearningCode0
A Meta-Learning Framework for Generalized Zero-Shot LearningCode0
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning—0
Transferable Contrastive Network for Generalized Zero-Shot Learning—0
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning—0
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning—0
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects—0
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Zero-shot Word Sense Disambiguation using Sense Definition EmbeddingsCode0
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