  We propose a new semi-supervised learning method of Variational AutoEncoder
(VAE) which yields a customized and explainable latent space by EXplainable
encoder Network (EXoN). Customization means a manual design of latent space
layout for specific labeled data. To improve the performance of our VAE in a
classification task without the loss of performance as a generative model, we
employ a new semi-supervised classification method called SCI (Soft-label
Consistency Interpolation). The classification loss and the Kullback-Leibler
divergence play a crucial role in constructing explainable latent space. The
variability of generated samples from our proposed model depends on a specific
subspace, called activated latent subspace. Our numerical results with MNIST
and CIFAR-10 datasets show that EXoN produces an explainable latent space and
reduces the cost of investigating representation patterns on the latent space.
