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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 141–150 of 161 papers

TitleStatusHype
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning—0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Generalized Zero-Shot Learning with Deep Calibration Network—0
Generalized Zero-Shot Recognition based on Visually Semantic Embedding—0
Generative Dual Adversarial Network for Generalized Zero-shot LearningCode0
Model Selection for Generalized Zero-shot Learning—0
Learning the Compositional Spaces for Generalized Zero-shot Learning—0
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features—0
From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process—0
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-ImportanceCode0
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