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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 81–90 of 161 papers

TitleStatusHype
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning—0
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Progressive Ensemble Networks for Zero-Shot Recognition—0
Semantic Borrowing for Generalized Zero-Shot Learning—0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Semantic Feature Extraction for Generalized Zero-shot Learning—0
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning—0
Synthetic Sample Selection for Generalized Zero-Shot Learning—0
Task Aligned Generative Meta-learning for Zero-shot Learning—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
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