Leveraging the Invariant Side of Generative Zero-Shot Learning

CVPR 2019 Jingjing LiMengmeng JinKe LuZhengming DingLei ZhuZi Huang

Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions... (read more)

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