A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
The amount of accessible raw data is ever-increasing in spite of the difficulty in obtaining a variety of labeled information; this makes semi-supervised learning a topic of practical importance. This paper proposes a novel regularization algorithm of an autoencoding deep neural network for semi-supervised learning. Given an input data, the deep neural network outputs the estimated label, and the remaining information called style. On the basis of the framework of a generative adversarialdoi:10.52731/iee.v3.i3.172 fatcat:wex7yrqt2re55eipkr2ydfpkl4