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Multimodal and Multiclass Semi-supervised Image-to-Image Translation

  • Jing BaiEmail author
  • Ran Chen
  • Hui Ji
  • Saisai Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

Abstract

In this paper, we propose a multimodal and multiclass semi-supervised image-to-image translation (MM-SSIT) framework to address the dilemma between expensive labeled work and diversity requirement of image translation. A cross-domain adversarial autoencoder is proposed to learn disentangled latent domain-invariant content codes and domain-specific style codes. The style codes are matched with a prior distribution so that we can generate a series of meaningful samples from the prior space. The content codes are embedded into a multiclass joint data distribution by an adversarial learning between a domain classifier and a category classifier so that we can generate multiclass images at one time. Consequently, multimodal and multiclass cross-domain images are generated by joint decoding the latent content codes and sampled style codes. Finally, the networks for MM-SSIT framework are designed and tested. Semi-supervised experiments with comparisons to state-of-art approach show that the proposed framework has the ability to generate high-quality and diversiform images in case of fewer labeled samples. Further experiments in the unsupervised setting demonstrate that MM-SSIT is superior in learning disentangled representation and domain adaption.

Keywords

Image-to-image translation Semi-supervised Adversarial auto encoder Adversarial learning 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (61762003), Natural Science Foundation of Ningxia (2018AAC03124), and Key R&D Program Projects of Ningxia 2019 (Research on Intelligent Assembly Technology Based on Multi-source Information Fusion).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.North Minzu UniversityYinchuanChina
  2. 2.Ningxia Provice Key Laboratory of Intelligent Information and Data ProcessingYinchuanChina

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