I am a researcher at NTT and a Ph.D student at Kyoto University (Kashima Lab.). My research interests are generative adversarial networks, representation learning, and transfer learning.

Updates

Activities

  • Reviewer: ICML 2022, NeurIPS 2022

Biography

Apr. 2022 - Current

Ph.D student at Dept. of Intelligence Science & Technology, Graduate School of Informatics, Kyoto University

Apr. 2017 - Current

Researcher at NTT

Apr. 2015 - Mar. 2017

M.E. from Dept. of Computer Engineering, Graduate School of Engineering, Yokohama National University

Apr. 2011 - Mar. 2015

B.E. from Dept. of Computer Engineering, Yokohama National University

Publications

Google Scholar

DBLP

International Conference

  1. D. Chijiwa, S. Yamaguchi, A. Kumagai, Y. Ida,
    Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks,
    Neural Information Processing Systems (NeurIPS), 2022.
  2. K. Adachi, S. Yamaguchi,
    Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations,
    IEEE International Conference on Multimedia & Expo (ICME), 2022.
  3. D. Chijiwa, S. Yamaguchi, Y. Ida, K. Umakoshi, T. Inoue,
    Pruning Randomly Initialized Neural Networks with Iterative Randomization,
    Neural Information Processing Systems (NeurIPS, Spotlight), 2021. [arXiv] [code]
  4. S. Yamaguchi, S. Kanai,
    F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain,
    International Conference on Computer Vision (ICCV), 2021. [arXiv]
  5. S. Yamaguchi, S. Kanai, T. Shioda, S. Takeda,
    Image Enhanced Rotation Prediction for Self-Supervised Learning,
    IEEE International Conference on Image Processing (ICIP), 2021. [arXiv]
  6. S. Kanai, M. Yamada, S. Yamaguchi, H. Takahashi, Y. Ida,
    Constraining Logits by Bounded Function for Adversarial Robustness,
    International Joint Conference on Neural Networks (IJCNN), 2021. [arXiv]
  7. S. Yamaguchi, S. Kanai, T. Eda,
    Effective Data Augmentation with Multi-Domain Learning GANs,
    AAAI Conference on Artificial Intelligence (AAAI), 2020. [arXiv]
  8. S. Yamaguchi, K. Kuramitsu,
    A Fusion Techniques of Schema and Syntax Rules for Validating Open Data,
    Asian Conference on Intelligent Information and Database Systems (ACIIDS), 2017

Preprints

  1. S. Kanai, S. Yamaguchi, M. Yamada, H. Takahashi, Y. Ida, Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness,
    arXiv, 2022.
  2. K. Adachi, S. Yamaguchi, A. Kumagai,
    Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation,
    arXiv, 2022.
  3. S. Yamaguchi, S. Kanai, A. Kumagai, D. Chijiwa, H. Kashima,
    Transfer Learning with Pre-trained Conditional Generative Models,
    arXiv, 2022.
  4. S. Yamaguchi, S. Kanai, T. Shioda, S. Takeda,
    Multiple pretext-task for self-supervised learning via mixing multiple image transformations,
    arXiv, 2019.
  5. K. Kuramitsu, S. Yamaguchi,
    XML Schema Validation using Parsing Expression Grammars,
    PeerJ PrePrints, 2015

Honors

  • Outstanding Reviewer: ICML 2022