  Zero-shot learning (ZSL) is concerned with the recognition of previously
unseen classes. It relies on additional semantic knowledge for which a mapping
can be learned with training examples of seen classes. While classical ZSL
considers the recognition performance on unseen classes only, generalized
zero-shot learning (GZSL) aims at maximizing performance on both seen and
unseen classes. In this paper, we propose a new process for training and
evaluation in the GZSL setting; this process addresses the gap in performance
between samples from unseen and seen classes by penalizing the latter, and
enables to select hyper-parameters well-suited to the GZSL task. It can be
applied to any existing ZSL approach and leads to a significant performance
boost: the experimental evaluation shows that GZSL performance, averaged over
eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from
28.2 to 57.1 on AwA2.
