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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

Showing 2130 of 161 papers

TitleStatusHype
Latent Embedding Feedback and Discriminative Features for Zero-Shot ClassificationCode1
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
Adaptive and Generative Zero-Shot LearningCode1
Feature Generating Networks for Zero-Shot LearningCode1
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest RadiographsCode1
FREE: Feature Refinement for Generalized Zero-Shot LearningCode1
Semantics Disentangling for Generalized Zero-Shot LearningCode1
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
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