no code implementations • 29 Sep 2021 • Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks.
1 code implementation • 25 Aug 2021 • Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi
Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks.
no code implementations • 11 May 2020 • Yuta Tokuoka, Shuji Suzuki, Yohei Sugawara
To evaluate the applicability of the ITL approach, we adopted the brain tissue annotation label on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the task of brain tumor segmentation on the target domain dataset of MRI.
no code implementations • 25 Oct 2019 • Yusuke Niitani, Toru Ogawa, Shuji Suzuki, Takuya Akiba, Tommi Kerola, Kohei Ozaki, Shotaro Sano
Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.
no code implementations • 1 Aug 2019 • Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent
Software frameworks for neural networks play a key role in the development and application of deep learning methods.
no code implementations • CVPR 2019 • Yusuke Niitani, Takuya Akiba, Tommi Kerola, Toru Ogawa, Shotaro Sano, Shuji Suzuki
However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector.
no code implementations • 4 Sep 2018 • Takuya Akiba, Tommi Kerola, Yusuke Niitani, Toru Ogawa, Shotaro Sano, Shuji Suzuki
We present a large-scale object detection system by team PFDet.
no code implementations • 12 Nov 2017 • Takuya Akiba, Shuji Suzuki, Keisuke Fukuda
We demonstrate that training ResNet-50 on ImageNet for 90 epochs can be achieved in 15 minutes with 1024 Tesla P100 GPUs.
1 code implementation • 31 Oct 2017 • Takuya Akiba, Keisuke Fukuda, Shuji Suzuki
One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs.