  Generalized zero-shot learning (GZSL) is one of the most realistic problems,
but also one of the most challenging problems due to the partiality of the
classifier to supervised classes. Instance-borrowing methods and synthesizing
methods solve this problem to some extent with the help of testing semantics,
but therefore neither can be used under the class-inductive instance-inductive
(CIII) training setting where testing data are not available, and the latter
require the training process of a classifier after generating examples. In
contrast, a novel method called Semantic Borrowing for improving GZSL methods
with compatibility metric learning under CIII is proposed in this paper. It
borrows similar semantics in the training set, so that the classifier can model
the relationship between the semantics of zero-shot and supervised classes more
accurately during training. In practice, the information of semantics of unseen
or unknown classes would not be available for training while this approach does
NOT need any information of semantics of unseen or unknown classes. The
experimental results on representative GZSL benchmark datasets show that it can
reduce the partiality of the classifier to supervised classes and improve the
performance of generalized zero-shot classification.
