A review of generalized zero-shot learning methods

Farhad Pourpanah, Moloud Abdar, Yuxuan LUO, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xizhao Wang, Q. M. Jonathan Wu

Abstract:

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. First, 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.