  In this article, we propose a transfer learning method for deep neural
networks (DNNs). Deep learning has been widely used in many applications.
However, applying deep learning is problematic when a large amount of training
data are not available. One of the conventional methods for solving this
problem is transfer learning for DNNs. In the field of image recognition,
state-of-the-art transfer learning methods for DNNs re-use parameters trained
on source domain data except for the output layer. However, this method may
result in poor classification performance when the amount of target domain data
is significantly small. To address this problem, we propose a method called
All-Transfer Deep Learning, which enables the transfer of all parameters of a
DNN. With this method, we can compute the relationship between the source and
target labels by the source domain knowledge. We applied our method to actual
two-dimensional electrophoresis image~(2-DE image) classification for
determining if an individual suffers from sepsis; the first attempt to apply a
classification approach to 2-DE images for proteomics, which has attracted
considerable attention as an extension beyond genomics. The results suggest
that our proposed method outperforms conventional transfer learning methods for
DNNs.
