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Unsupervised Representation Learning Based on Generative Adversarial Networks

  • Shi Xu
  • Jia WangEmail author
Conference paper
  • 2 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)

Abstract

This paper introduces a novel model for learning disentangled representations based on Generative Adversarial Networks. The training model is unsupervised without identity information. Unlike InfoGAN in which the disentangled representation is learnt by getting the variational lower bound of the mutual information indirectly, our method introduces a direct way by adding predicting networks and encoder into GANs and measuring the correlation among the encoder outputs. Experiment results on MNIST demonstrate that the proposed model is more generalizable and robust than InfoGAN. With experiments on Celeba-HQ, we show that our model can extract factorial features with complicate datasets and produce results comparable to supervised models.

Keywords

Unsupervised representation learning Disentangled features Generative Adversarial Networks 

Notes

Acknowledgements

We thank the reviewers. This work is supported in part by the Chinese Science Foundation under grant 61771305.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina

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