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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 101–110 of 161 papers

TitleStatusHype
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot LearningCode1
Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders—0
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot LearningCode1
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
Class Normalization for (Continual)? Generalized Zero-Shot LearningCode1
Zero-Shot Learning with Common Sense Knowledge GraphsCode1
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention—0
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Improving Generalized Zero-Shot Learning by Semantic Discriminator—0
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