  Zero-shot learning (ZSL) aims to recognize unseen object classes without any
training samples, which can be regarded as a form of transfer learning from
seen classes to unseen ones. This is made possible by learning a projection
between a feature space and a semantic space (e.g. attribute space). Key to ZSL
is thus to learn a projection function that is robust against the often large
domain gap between the seen and unseen classes. In this paper, we propose a
novel ZSL model termed domain-invariant projection learning (DIPL). Our model
has two novel components: (1) A domain-invariant feature self-reconstruction
task is introduced to the seen/unseen class data, resulting in a simple linear
formulation that casts ZSL into a min-min optimization problem. Solving the
problem is non-trivial, and a novel iterative algorithm is formulated as the
solver, with rigorous theoretic algorithm analysis provided. (2) To further
align the two domains via the learned projection, shared semantic structure
among seen and unseen classes is explored via forming superclasses in the
semantic space. Extensive experiments show that our model outperforms the
state-of-the-art alternatives by significant margins.
