281 citations · 391 across the 5 of their papers we have counts for
12 papers
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…
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase +2
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that a…
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe +2
Recomputation algorithms collectively refer to a family of methods that aims to reduce the memory consumption of the backpropagation by selectively discarding the intermediate resu…
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…