Ryuichiro Hataya's Webpage

About

PhD student at UTokyo and RA at RIKEN AIP / focusing on DL and ML

Our paper “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval” is accepted at ML4H 2020.
Our research “MADAO: Meta Approach to Data Augmentation Optimization” won Best Student Paper Award at MIRU 2020.
Our paper “Faster AutoAugment: Learning Augmentation Strategies using Backpropagation” is accepted at ECCV 2020. I will present on UTC+1_8/26 0am, 5am (JST_8/26 8am, 2pm).

All Posts

Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Faster AutoAugment: Learning Augmentation Strategies using Backpropagation”, European Conference on Computer Vision, 2020.
Ryuichiro Hataya, and Hideki Nakayama, “LOL: Learning To Optimize Loss Switching Under Label Noise”, International Conference on Image Processing, 2019.

Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Meta Approach to Data Augmentation Optimization”, 2020. arXiv

Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, and Ryuji Hamamoto, “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval.” Machine Learning for Health Workshop at NeurIPS 2020. (Peer Reviewed, Extended Abstract)
Ryuichiro Hataya, Kumiko Matsui, Tomoki Karasawa, “Learning to Identify Large Fossils using Deep Convolutional Neural Networks”, Geological Society of America Abstracts with Programs. Vol 52, No. 6, 2020.

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