Takeru Miyato

Takeru Miyato

(Last updated: Dec.09, 2022)

Ph.D. student @ Autonomous Vision Group, University of Tübingen


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


Google Scholar


I am curretnly a Ph.D. student at Autonomous Vision Group, University of Tübingen under ELLIS Ph.D program. I am supervised by Andreas Geiger (UTübingen) and Max Welling (UvA). I am also working for Preferred Networks, Inc. as a part-time researcher.
I worked at ATR, Google Brain (as an intern). I did my Master at Integrated System Biology Labratory, 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


Selected Publications

For the full publication list, please see my Google Scholar profile

  1. Takeru Miyato*, Masanori Koyama*, and Kenji Fukumizu
    Unsupervised Learning of Equivariant Structure from Sequences.
    NeurIPS. 2022.
    [code] [arXiv] [OpenReview] [poster]
  2. Takeru Miyato, Masanori Koyama
    Generative Adversarial Networks.
    A book chapter in Computer Vision: A Reference Guide. Ed. by Katsushi Ikeuchi. Springer. 2021
    [paper link]
  3. Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato
    Robustness to adversarial perturbations in learning from incomplete data.
    NeurIPS. 2019
  4. [code] [paper link]
  5. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama and Shin Ishii
    Virtual Adversarial Training : A Regularization Method for Supervised and Semi-Supervised Learning.
    IEEE TPAMI, 2019. (extended version of the paper published at ICLR2016)
    [code (TF)] [code (Chainer)] [arXiv]
  6. 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]
  7. Takeru Miyato and Masanori Koyama
    cGANs with Projection Discriminator.
    ICLR, 2018.
    [code] [OpenReview]
  8. 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]
  9. 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]
  10. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae and Shin Ishii
    Distributional Smoothing with Virtual Adversarial Training.
    ICLR, 2016.
    [code] [arXiv] [slide] [poster]

Working Experiences


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

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