  Existing zero-shot learning (ZSL) methods usually learn a projection function
between a feature space and a semantic embedding space(text or attribute space)
in the training seen classes or testing unseen classes. However, the projection
function cannot be used between the feature space and multi-semantic embedding
spaces, which have the diversity characteristic for describing the different
semantic information of the same class. To deal with this issue, we present a
novel method to ZSL based on learning class label autoencoder (CLA). CLA can
not only build a uniform framework for adapting to multi-semantic embedding
spaces, but also construct the encoder-decoder mechanism for constraining the
bidirectional projection between the feature space and the class label space.
Moreover, CLA can jointly consider the relationship of feature classes and the
relevance of the semantic classes for improving zero-shot classification. The
CLA solution can provide both unseen class labels and the relation of the
different classes representation(feature or semantic information) that can
encode the intrinsic structure of classes. Extensive experiments demonstrate
the CLA outperforms state-of-art methods on four benchmark datasets, which are
AwA, CUB, Dogs and ImNet-2.
