Takeru Miyato

(Last updated: 10/24, 2018)

Researcher at Preferred Networks, Inc.


E-mail : miyato(at)preferred.jp, takeru.miyato(at)gmail.com


Google Scholar


I am now working at Preferred Networks, Inc. as a researcher.
I was Master student in Integrated System Biology Labratory,
Department of System Science, Graduate school of Informatics, Kyoto University.

Research Interests

I am engaged in machine learning research.
I have strong interest in scalable and simple machine learning algorithm.
My current focuses are



  1. Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato and Daisuke Okanohara
    Neural Multi-scale Image Compression.
    ACCV, 2018. (accepted for oral presentation).
  2. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama and Shin Ishii
    Virtual Adversarial Training : A Regularization Method for Supervised and Semi-Supervised Learning.
    IEEE TPAMI, 2018. (extended version of the paper published at ICLR2016)
    [code (TF)] [code (Chainer)] [arXiv]
  3. Takeru Miyato, Toshiki Kataoka, Masanori Koyama and Yuichi Yoshida
    Spectral Normalization for Generative Adversarial Networks.
    ICLR, 2018. (accepted for oral presentation)
    [code] [code (on ImageNet)] [OpenReview]
  4. Takeru Miyato and Masanori Koyama
    cGANs with Projection Discriminator.
    ICLR, 2018.
    [code] [OpenReview]
  5. Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto and Masashi Sugiyama
    Learning Discrete Representations via Information Maximizing Self Augmented Training.
    ICML, 2017.
    [code (by @weihua916)] [arXiv]
  6. Takeru Miyato, Andrew M. Dai and Ian Goodfellow
    Adversarial Training Methods for Semi-Supervised Text Classification.
    ICLR, 2017.
    [code (Chainer, by @aonotas)] [code (TF)] [arXiv] [slide] [poster] [OpenReview]
  7. Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda and Masanori Koyama
    Synthetic gradient methods with Virtual Forward-Backward Networks.
    Workshop at ICLR, 2017
    [poster] [OpenReview]
  8. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae and Shin Ishii
    Distributional Smoothing with Virtual Adversarial Training.
    ICLR, 2016.
    [code] [arXiv] [slide] [poster]


  1. Jiren Jin, Richard G. Calland, Takeru Miyato, Brian K. Vogel and Hideki Nakayama
    Parameter Reference Loss for Unsupervised Domain Adaptation.
    arXiv preprint arXiv:1711.07170, 2017.
  2. Yuichi Yoshida and Takeru Miyato
    Spectral Norm Regularization for Improving the Generalizability of Deep Learning.
    arXiv preprint arXiv:1705.10941, 2017.

Working Experiences

Teaching Experiences


Python, MATLAB, Objective-C, C#, C, C++, OpenGL.

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