A Review of Generalized Zero-Shot Learning Methods

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.

Automated summary

Generalized zero-shot learning (GZSL) trains a model to classify data samples when some output classes are unknown. Semantic information of seen and unseen classes is used to bridge the gap between them. This review paper provides an overview, discusses categorization and representative methods, benchmark datasets, applications, and research gaps of GZSL.