Host: The Japanese Society for Artificial Intelligence
Name : 34th Annual Conference, 2020
Number : 34
Location : Online
Date : June 09, 2020 - June 12, 2020
The our purpose is to develop a system for identify whether it is a risk factor or not from ground penetrating radar images of underground objects. In order to train the CNN whether a radar image is a risk factor, a large number of radar images with risk factor labels required, but in reality, a large number of unlabeled radar images and only a few labeled radar images can be obtained. In this study, we report that the recognition rate can be improved by perform the supervised learning a small number of labeled radar images with MLP after the unsupervised learning of a large number of unlabeled radar images with VAE.