GUANG LI Guang Li

Short Biography: Guang Li is a Faculty Member of the Laboratory of Media Dynamics (LMD), a Specially Appointed Assistant Professor of the Education and Research Center for Mathematical and Data Science (MDSC), and a Collaborative Researcher of the Data-Driven Interdisciplinary Research Emergence Department (D-RED), at Hokkaido University. He earned his B.S. degree in Software Engineering and B.A. dual degree in Japanese from Dalian University of Technology, China, in 2019, and his M.S. and Ph.D. degrees both in Information Science from Hokkaido University, Japan, in 2022 and 2023, respectively. His research interests include Dataset Distillation, Self-Supervised Learning, Data-Centric AI, and Medical Image Analysis.

His research has been featured in renowned media, including Scientific American, Nikkei, NHK, Synced, AI Era, Deccan Herald, and Analytics India Magazine. He curates and maintains the Awesome Dataset Distillation project, which has received over 1K stars and is well-known in the field. He co-organized the 1st Dataset Distillation Workshop at CVPR 2024 and the 1st Dataset Distillation Challenge at ECCV 2024. He has served as Area Chair for ACM MM, as well as a Program Committee Member for top-tier conferences such as NeurIPS, CVPR, ECCV, AAAI, and MICCAI. He also served as a Reviewer for prestigious journals such as TIP, TMI, MedIA, TNNLS, NN, TMM, and TCSVT. He is a member of IEEE.

I’m currently open to collaborating on dataset distillation and related topics! If you have any ideas, please feel free to drop me an email.

        

News

[2024/04] Our proposal to host the 1st Dataset Distillation Challenge was accepted to ECCV 2024! This event will be co-hosted by 19 renowned universities and research institutions from 7 countries, including MIT, Stanford, and Google! Many thanks to Prof. Yang You for the invitation!

[2024/04] One paper on Dataset Distillation was accepted to CVPR 2024 Workshop! Contrats to Longzhen!

[2024/03] Awesome Dataset Distillation project now has its official Homepage! Many thanks to Longzhen!

[2024/02] Call for Papers: Please check the 1st Workshop on Dataset Distillation at CVPR 2024! I will serve as a Committee Member for this event! Many thanks to Dr. Saeed Vahidian for the invitation!

[2024/01] Invited to serve as an Area Chair for ACM MM 2024!

[2024/01] One paper on Dataset Distillation was accepted to Neural Networks!

[2024/01] Awesome Dataset Distillation project reached 1K stars on GitHub!

Biography

  • 2023/10 ~ Present Hokkaido University, Specially Appointed Assistant Professor
  • 2022/04 ~ 2023/09 Hokkaido University, Ph.D. in Information Science
  • 2020/04 ~ 2022/03 Hokkaido University, M.S. in Information Science
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.A. in Japanese
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.S. in Software Engineering

Highlights

Awesome Dataset Distillation

The 1st Dataset Distillation Challenge

The 1st Dataset Distillation Workshop

Selected Publications (Full List)

Journal

  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Importance-aware adaptive dataset distillation,” Neural Networks (NN), vol. 172, 106154, 2024. [arXiv] [Paper]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised learning for gastritis detection with gastric X-ray images,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), vol. 18, no. 10, pp. 1841-1848, 2023. [arXiv] [Paper]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling,” Computers in Biology and Medicine (CIBM), vol. 158, 106877, 2023. [arXiv] [Paper]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on self-supervised transfer learning using chest X-ray images,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), vol. 18, no. 4, pp. 715-722, 2023. [arXiv] [Paper]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Compressed gastric image generation based on soft-label dataset distillation for medical data sharing,” Computer Methods and Programs in Biomedicine (CMPB), vol. 227, 107189, 2022. [arXiv] [Paper]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum,” Multimedia Tools and Applications (MTAP), vol. 81, no. 22, pp. 32287-32303, 2022. [arXiv] [Paper]

Conference

  • Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Generative dataset distillation: balancing global structure and local details,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, pp. 1-8, Seattle, WA, USA, 2024. [Link] (Accepted)
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” AAAI Conference on Artificial Intelligence (AAAI) Workshop, pp. 1-6, Washington, DC, USA, 2023. [arXiv] [Link]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-knowledge distillation based self-supervised learning for COVID-19 detection from chest X-ray images,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1371-1375, Singapore, 2022. [arXiv] [Paper] [Link]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “TriBYOL: Triplet BYOL for self-supervised representation learning,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3458-3462, Singapore, 2022. (Top 10 self-supervised learning models in 2022) [arXiv] [Paper] [Link]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Soft-label anonymous gastric X-ray image distillation,” IEEE International Conference on Image Processing (ICIP), pp. 305-309, Abu Dhabi, UAE, 2020. (The first paper to explore real-world dataset distillation) [arXiv] [Paper] [Link]
  • Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Complexity evaluation of medical image data for classification problem based on spectral clustering,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 667-669, Kobe, Japan, 2020. (Best Student Paper Award) [Paper]

Awards

  • The 2023 IEEE Sapporo Section Encouragement Award (2024/02, ICASSP 2022)
  • The Dean’s Award (2023/09, Ph.D. Degree)
  • The First Hokkaido University Medical AI Symposium Outstanding Research Award (2022/11)
  • IEICE Hokkaido Section Student Encouragement Award (2022/03, M.S. Degree)
  • IEEE GCCE 2020 Best Student Paper Award (2020/10)
  • Outstanding Graduate of Dalian University of Technology (2019/06, B.S. and B.A. Degrees)

Funding

  • Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research (2022/04 ~ 2023/09) [Link]

Media Coverages

  • “CO2 60トン吸収認証 えりものブルーカーボン 雑海藻駆除しコンブ増,” 北海道新聞, 2024/01/10. [Link]
  • “CO2吸収量、森林の9倍 えりものコンブ 町の調査報告,” 北海道新聞, 2023/09/27. [Link]
  • “コンブで脱炭素推進 開発局がCO2吸収量初調査 漁業者の収入増も狙う,” 北海道新聞, 2023/07/01. [Link]
  • “えりも沿岸域でブルーカーボン 町などが検討会,” 北海道建設新聞, 2023/06/09. [Link]
  • “コンブのCO2吸収量、年内にも算出 えりも町など検討会発足,” 北海道新聞, 2023/06/07. [Link]
  • “えりも町 海中で二酸化炭素を吸収「ブルーカーボン」の検討会,” NHK NEWS WEB, 2023/06/07. [Link]
  • “北海道開発局、えりも町で「ブルーカーボン」調査,” 日本経済新聞, 2023/04/26. [Link]
  • “2022 Top10自监督学习模型发布! 美中两国8项成果霸榜,” 新智元, 2022/11/12. [Link]
  • “Top 10 Self-supervised Learning Models in 2022,” Analytics India Magazine, 2022/11/02. [Link]
  • “一个项目帮你了解数据集蒸馏Dataset Distillation,” 机器之心, 2022/10/09. [Link]
  • “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. [Link]
  • “AIでインフラ点検 北海道大情報科学研究院,” 日本経済新聞, 2021/06/16. [Link]
  • “How to Make Artificial Intelligence More Democratic,” Scientific American, 2021/01/02. [Link]

Academic Services

Organizer

  • The 1st Workshop on Dataset Distillation at CVPR 2024
  • The 1st Dataset Distillation Challenge at ECCV 2024

Conference Area Chair

  • ACM International Conference on Multimedia (ACM MM)

Conference Program Committee Member

  • Conference on Neural Information Processing Systems (NeurIPS)
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • European Conference on Computer Vision (ECCV)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
  • IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Journal Reviewer

  • IEEE Transactions on Imaging Processing
  • IEEE Transactions on Medical Imaging
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Affective Computing
  • IEEE Transactions on Artificial Intelligence
  • Medical Image Analysis
  • Neural Networks
  • Pattern Recognition
  • 10+ Journals

Visitors

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