  Zero-shot learning (ZSL) is a challenging problem that aims to recognize the
target categories without seen data, where semantic information is leveraged to
transfer knowledge from some source classes. Although ZSL has made great
progress in recent years, most existing approaches are easy to overfit the
sources classes in generalized zero-shot learning (GZSL) task, which indicates
that they learn little knowledge about target classes. To tackle such problem,
we propose a novel Transferable Contrastive Network (TCN) that explicitly
transfers knowledge from the source classes to the target classes. It
automatically contrasts one image with different classes to judge whether they
are consistent or not. By exploiting the class similarities to make knowledge
transfer from source images to similar target classes, our approach is more
robust to recognize the target images. Experiments on five benchmark datasets
show the superiority of our approach for GZSL.
