5 citations · 13 across the 16 of their papers we have counts for
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cs.LG2023★ 1 cited
Regularizing Neural Networks with Meta-Learning Generative Models
Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai +2
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentation leverages the synthetic samples produced by generative mo…
cs.LG2023
Toward Data Efficient Model Merging between Different Datasets without Performance Degradation
Masanori Yamada, Tomoya Yamashita, Shin'ya Yamaguchi +1
Model merging is attracting attention as a novel method for creating a new model by combining the weights of different trained models. While previous studies reported that model me…
cs.LG2023★ 1 cited
Transferring Learning Trajectories of Neural Networks
Daiki Chijiwa
Training deep neural networks (DNNs) is computationally expensive, which is problematic especially when performing duplicated or similar training runs in model ensemble or fine-tun…