5 citations · 5 across the 3 of their papers we have counts for
4 papers
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…
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
He Huang, Shunta Saito, Yuta Kikuchi +3
Scene graph parsing aims to detect objects in an image scene and recognize their relations. Recent approaches have achieved high average scores on some popular benchmarks, but fail…
Phase transition encoded in neural network
Kouji Kashiwa, Yuta Kikuchi, Akio Tomiya
We discuss an aspect of neural networks for the purpose of phase transition detection. To this end, we first train the neural network by feeding Ising/Potts configurations with lab…
Neural Sequence Model Training via -divergence Minimization
Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura +2
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maxi…