  Zero-shot learning (ZSL) which aims to recognize unseen object classes by
only training on seen object classes, has increasingly been of great interest
in Machine Learning, and has registered with some successes. Most existing ZSL
methods typically learn a projection map between the visual feature space and
the semantic space and mainly suffer which is prone to a projection domain
shift primarily due to a large domain gap between seen and unseen classes. In
this paper, we propose a novel inductive ZSL model based on projecting both
visual and semantic features into a common distinct latent space with
class-specific knowledge, and on reconstructing both visual and semantic
features by such a distinct common space to narrow the domain shift gap. We
show that all these constraints on the latent space, class-specific knowledge,
reconstruction of features and their combinations enhance the robustness
against the projection domain shift problem, and improve the generalization
ability to unseen object classes. Comprehensive experiments on four benchmark
datasets demonstrate that our proposed method is superior to state-of-the-art
algorithms.
