News

I’m thrilled to announce the release of my new book, “Image Generation with Python”! With the rapid advancements in technology, image generation has become more accessible than ever. This comprehensive guide delves into the fascinating world of image generation, focusing on diffusion models and Stable Diffusion 🌟

The following paper has been accepted to the IEEE Access. This paper is an extended version of the content presented at the “Practical ML for Low Resource Settings Workshop @ ICLR 2024”.

The following paper has been accepted to the ECCV2024. Shoma Iwai, Atsuki Osanai, Shunsuke Kitada, and Shinichiro Omachi. “Layout-Corrector: Alleviating Layout Sticking Phenomenon in Discrete Diffusion Model”.

Our team provided and published the following technical map at SSII2024 session. 🌐 https://bit.ly/ssii2024-techmap-creative-design-map 📃 https://bit.ly/ssii2024-techmap-creative-design-sheet See this post for more details.

The following paper has been accepted at Practical ML for Low Resource Settings Workshop (PM4LRS) @ ICLR 2024. Sota Nemoto, Shunsuke Kitada, and Hitoshi Iyatomi. “Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition”

A special issue of Journal of JSAI on doctoral dissertations, including a summary of my dissertation, is now available: https://www.jstage.jst.go.jp/article/jjsai/39/1/39_50/_article/-char/ja/

I am thrilled to announce that I have successfully defended my PhD on March 24, 2023. The journey that began in the Intelligent Information Processing Lab has been nothing short of remarkable and exhilarating.

Got Hosei University Research Grant for Doctoral Course Adopters in 2022 (480,000 JPY).

The following paper has been accepted to the IEEE Access. Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, and Hitoshi Iyatomi. “DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention”

The following paper has been accepted to the Springer Applied Intelligence. Shunsuke Kitada and Hitoshi Iyatomi. “Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training”