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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 51–60 of 161 papers

TitleStatusHype
Attribute-Aware Representation Rectification for Generalized Zero-Shot LearningCode0
Multi-modal Cycle-consistent Generalized Zero-Shot LearningCode0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Data-Free Generalized Zero-Shot LearningCode0
Alleviating Feature Confusion for Generative Zero-shot LearningCode0
Generative Dual Adversarial Network for Generalized Zero-shot LearningCode0
Data Distribution Distilled Generative Model for Generalized Zero-Shot RecognitionCode0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
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