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Koshiro Nagano
Koshiro Nagano is a Computer Vision researcher at Keio University focused on synthetic-to-real transfer learning, and continual learning.
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Posts
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Blog Post number 4
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Noise Reduction of SEM Images using U-net with SSIM Loss Function
Published in ISCIE SSS 2020, 2021
We propose a U-Net-based noise reduction method for SEM images trained with an SSIM loss function.
Recommended citation: K. Nagano, Y. Mukouyama, T. Nishimura, H. Fujioka, K. Watanabe, T. Kurita, A. Hidaka, "Noise Reduction of SEM Images using U-net with SSIM Loss Function," ISCIE SSS 2020, 2020.
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Frozen Network Few-Shot Object Detection
Published in ICIP 2025, 2025
We propose a generalized few-shot object detection method that freezes the pretrained backbone network during fine-tuning.
Recommended citation: K. Nagano, F. Sato, R. Hachiuma, K. Tsutsukawa, T. Sekii, "Frozen Network Few-Shot Object Detection," ICIP, 2025.
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Learning from Synthetic Data via Provenance-Based Input Gradient Guidance
Published in CVPR 2026, 2026
We propose a training method that leverages provenance-based input gradient guidance to improve learning from synthetic data.
Recommended citation: K. Nagano, R. Fujii, R. Hachiuma, F. Sato, T. Sekii, H. Saito, "Learning from Synthetic Data via Provenance-Based Input Gradient Guidance," CVPR, 2026.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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