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A Joint Generative Model for Zero-Shot Learning

  • Rui Gao
  • Xingsong HouEmail author
  • Jie Qin
  • Li Liu
  • Fan Zhu
  • Zhao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Zero-shot learning (ZSL) is a challenging task due to the lack of data from unseen classes during training. Existing methods tend to have the strong bias towards seen classes, which is also known as the domain shift problem. To mitigate the gap between seen and unseen class data, we propose a joint generative model to synthesize features as the replacement for unseen data. Based on the generated features, the conventional ZSL problem can be tackled in a supervised way. Specifically, our framework integrates Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) conditioned on class-level semantic attributes for feature generation based on element-wise and holistic reconstruction. A categorization network acts as the additional guide to generate features beneficial for the subsequent classification task. Moreover, we propose a perceptual reconstruction loss to preserve semantic similarities. Experimental results on five benchmarks show the superiority of our framework over the state-of-the-art approaches in terms of both conventional ZSL and generalized ZSL settings.

Keywords

Zero-shot learning Variational autoencoder Generative adversarial network Perceptual reconstruction 

Notes

Acknowledgements

This work was supported in part by the NSFC under Grant 61872286, u1531141, 61732008, 61772407 and 61701391, the National Key R&D Program of China under Grant 2017YFF0107700, the National Science Foundation of Shaanxi Province under Grant 2018JM6092, and Guangdong Provincial Science and Technology Plan Project under Grant 2017A010101006 and 2016A010101005.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rui Gao
    • 1
  • Xingsong Hou
    • 1
    • 5
    Email author
  • Jie Qin
    • 2
  • Li Liu
    • 3
  • Fan Zhu
    • 3
  • Zhao Zhang
    • 4
  1. 1.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Computer Vision LaboratoryETH ZurichZürichSwitzerland
  3. 3.Inception Institute of Artificial IntelligenceAbu DhabiUAE
  4. 4.Soochow UniversitySuzhouChina
  5. 5.Guangdong Xi’an Jiaotong University AcademyGuangdongChina

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