Recent research on Generalized Zero-Shot Learning (GZSL) has focused
primarily on generation-based methods. However, current literature has
overlooked the fundamental principles of these methods and has made limited
progress in a complex manner. In this paper, we aim to deconstruct the
generator-classifier framework and provide guidance for its improvement and
extension. We begin by breaking down the generator-learned unseen class
distribution into class-level and instance-level distributions. Through our
analysis of the role of these two types of distributions in solving the GZSL
problem, we generalize the focus of the generation-based approach, emphasizing
the importance of (i) attribute generalization in generator learning and (ii)
independent classifier learning with partially biased data. We present a simple
method based on this analysis that outperforms SotAs on four public GZSL
datasets, demonstrating the validity of our deconstruction. Furthermore, our
proposed method remains effective even without a generative model, representing
a step towards simplifying the generator-classifier structure. Our code is
available at https://github.com/cdb342/DGZ.

本文针对广义零样本学习领域内的生成模型方法进行了探究和拆解，并提出了基于类级别和实例级别分布的属性泛化及部分偏置数据的独立分类器学习方法。实验证明本文提出的方法在公共数据集上性能优于当前最先进算法，同时该方法即使没有生成模型仍然有效，这是对生成 - 分类器结构的进一步简化。