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Fingerprint Dive into the research topics where Hideaki Hayashi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Neural networks Engineering & Materials Science
Electromyography Medicine & Life Sciences
Time series Engineering & Materials Science
Music Therapy Medicine & Life Sciences
Electroencephalography Engineering & Materials Science
Pattern recognition Engineering & Materials Science
Backpropagation Engineering & Materials Science
Experiments Engineering & Materials Science

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Research Output 2013 2020

  • 37 Citations
  • 3 h-Index
  • 11 Conference contribution
  • 11 Article
  • 2 Chapter

Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection

Han, C., Rundo, L., Araki, R., Furukawa, Y., Mauri, G., Nakayama, H. & Hayashi, H., Jan 1 2020, Smart Innovation, Systems and Technologies. Springer Science and Business Media Deutschland GmbH, p. 291-303 13 p. (Smart Innovation, Systems and Technologies; vol. 151).

Research output: Chapter in Book/Report/Conference proceedingChapter

Magnetic resonance
Tumors
Brain
Medical imaging
Neural networks

A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction

Hayashi, H. & Uchida, S., Jan 1 2019, Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers. Jawahar, C. V., Li, H., Schindler, K. & Mori, G. (eds.). Springer Verlag, p. 414-430 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11362 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Autocorrelation
Multiplication
Neural networks
Backpropagation
Pixels

GlyphGAN: Style-consistent font generation based on generative adversarial networks

Hayashi, H., Abe, K. & Uchida, S., Jan 1 2019, (Accepted/In press) In : Knowledge-Based Systems. 186, 104927.

Research output: Contribution to journalArticle

Neural networks
Generator

An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models

Furui, A., Hayashi, H. & Tsuji, T., 2018, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

An EMG Pattern Classification Method Based on a Mixture of Variance Distribution Models

Furui, A., Hayashi, H. & Tsuji, T., Oct 26 2018, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., p. 5216-5219 4 p. 8513446. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Electromyography
Pattern recognition
Stochastic models
Uncertainty
Random variables