Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning

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Yunyun Wang, Jian Mao, Chenguang Guo, Songcan Chen

Published: 2024, Last Modified: 06 Nov 2025Neural Networks 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•Prototype-guided generation is proposed in PGZSL for relieving class bias in generator.•Certainty-Driven Mixup is proposed in PGZSL for suppressing boundary data generation.•Empirical results show the effectiveness of PGZSL for both ZSL and GZSL.