Deconstructed Generation-Based Zero-Shot Model

Dubing Chen; Yuming Shen; Haofeng Zhang; Philip H. S. Torr

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arXiv · 2204.11280

Deconstructed Generation-Based Zero-Shot Model

Dubing Chen·Yuming Shen·Haofeng Zhang·Philip H. S. Torr

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Abstract

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}.

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BibTeX RIS

Dubing Chen, Yuming Shen, Haofeng Zhang, Philip H. S. Torr. 2023-03-07. Deconstructed Generation-Based Zero-Shot Model. https://arxiv.org/abs/2204.11280

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