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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
Learning shared manifold representation of images and attributes for generalized zero-shot learning—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Learning the Compositional Spaces for Generalized Zero-shot Learning—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
Generative Dual Adversarial Network for Generalized Zero-shot LearningCode0
Generalized Zero-Shot Learning Via Over-Complete DistributionCode0
Zero-Shot Learning by Harnessing Adversarial SamplesCode0
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