  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 \url{https://github.com/cdb342/DGZ}.
