Ryota Yoshihashi — accepted AI-conference papers

2026

Teacher-Guided Routing for Sparse Vision Mixture-of-Experts

Masahiro Kada, Ryota Yoshihashi, Satoshi Ikehata, Rei Kawakami, Ikuro Sato

CVPR 2026

Recent progress in deep learning has been driven by increasingly large-scale models, but the resulting computational cost has become a critical bottleneck. Sparse Mixture of Experts (MoE) offers an effective solution by activating only a small subset of experts for each input, achieving high scalabi

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2019

Classification-Reconstruction Learning for Open-Set Recognition

Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura

CVPR 2019poster

Open-set classification is a problem of handling 'unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on know…

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