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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
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification—0
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning—0
A Review of Open-World Learning and Steps Toward Open-World Learning Without LabelsCode0
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
Bias-Awareness for Zero-Shot Learning the Seen and UnseenCode0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders—0
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