5 citations · 5 across the 1 of their papers we have counts for
2 papers
cs.LG2021★ 5 cited
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…
cs.LG2018
Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash
Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U-chupala +2
Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchro…