  The existing Zero-Shot learning (ZSL) methods may suffer from the vague class
attributes that are highly overlapped for different classes. Unlike these
methods that ignore the discrimination among classes, in this paper, we propose
to classify unseen image by rectifying the semantic space guided by the visual
space. First, we pre-train a Semantic Rectifying Network (SRN) to rectify
semantic space with a semantic loss and a rectifying loss. Then, a Semantic
Rectifying Generative Adversarial Network (SR-GAN) is built to generate
plausible visual feature of unseen class from both semantic feature and
rectified semantic feature. To guarantee the effectiveness of rectified
semantic features and synthetic visual features, a pre-reconstruction and a
post reconstruction networks are proposed, which keep the consistency between
visual feature and semantic feature. Experimental results demonstrate that our
approach significantly outperforms the state-of-the-arts on four benchmark
datasets.
