Takeru Miyato

Takeru Miyato

(Last updated: Dec.09, 2022)

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

Contact

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

Links

GitHub
Google Scholar
ResearchGate

Affiliation

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

Education

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

Skills

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

Hosted on GitHub Pages — Theme by orderedlist