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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 51–60 of 161 papers

TitleStatusHype
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning—0
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning—0
Semantic Feature Extraction for Generalized Zero-shot Learning—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning—0
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning—0
Dual Progressive Prototype Network for Generalized Zero-Shot Learning—0
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