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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 91–100 of 161 papers

TitleStatusHype
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
A Review of Open-World Learning and Steps Toward Open-World Learning Without LabelsCode0
A Review of Generalized Zero-Shot Learning MethodsCode1
Learning Graph-Based Priors for Generalized Zero-Shot Learning—0
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures—0
A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Generalized Zero-Shot Learning via VAE-Conditioned Generative FlowCode1
Bias-Awareness for Zero-Shot Learning the Seen and UnseenCode0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
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