Leveraging the Invariant Side of Generative Zero-Shot Learning

1 code implementation • CVPR 2019 • Jingjing Li, Mengmeng Jin, Ke Lu, Zhengming Ding, Lei Zhu, Zi Huang

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.

Generalized Zero-Shot Learning