281 citations · 407 across the 9 of their papers we have counts for
9 papers
PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency
Preferred Elements, :, Kenshin Abe +18
We introduce PLaMo-100B, a large-scale language model designed for Japanese proficiency. The model was trained from scratch using 2 trillion tokens, with architecture such as QK No…
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
Hiroaki Mikami, Kenji Fukumizu, Shogo Murai +5
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. The most…
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation
Yuta Tokuoka, Shuji Suzuki, Yohei Sugawara
With recent advances in supervised machine learning for medical image analysis applications, the annotated medical image datasets of various domains are being shared extensively. G…
Team PFDet's Methods for Open Images Challenge 2019
Yusuke Niitani, Toru Ogawa, Shuji Suzuki +4
We present the instance segmentation and the object detection method used by team PFDet for Open Images Challenge 2019. We tackle a massive dataset size, huge class imbalance and f…
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intend…
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
Yusuke Niitani, Takuya Akiba, Tommi Kerola +3
Efficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large d…
PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
Takuya Akiba, Tommi Kerola, Yusuke Niitani +3
We present a large-scale object detection system by team PFDet. Our system enables training with huge datasets using 512 GPUs, handles sparsely verified classes, and massive class…
Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes
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. This was made possible by using a large minibatch size of…
ChainerMN: Scalable Distributed Deep Learning Framework
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. Enabling the use of further computing ab…