  Generalized zero-shot learning (GZSL) aims to train a model for classifying
data samples under the condition that some output classes are unknown during
supervised learning. To address this challenging task, GZSL leverages semantic
information of the seen (source) and unseen (target) classes to bridge the gap
between both seen and unseen classes. Since its introduction, many GZSL models
have been formulated. In this review paper, we present a comprehensive review
on GZSL. Firstly, we provide an overview of GZSL including the problems and
challenges. Then, we introduce a hierarchical categorization for the GZSL
methods and discuss the representative methods in each category. In addition,
we discuss the available benchmark data sets and applications of GZSL, along
with a discussion on the research gaps and directions for future
investigations.
