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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 131–140 of 161 papers

TitleStatusHype
Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning—0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
CLAREL: Classification via retrieval loss for zero-shot learning—0
Learning shared manifold representation of images and attributes for generalized zero-shot learning—0
Leveraging the Invariant Side of Generative Zero-Shot LearningCode0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual RecognitionCode0
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning—0
Multi-modal Ensemble Classification for Generalized Zero Shot Learning—0
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