EXoN: EXplainable encoder Network

• URL: http://arxiv.org/abs/2105.10867v1
• Date: Sun, 23 May 2021 07:04:30 GMT
• Title: EXoN: EXplainable encoder Network
• Authors: SeungHwan An, Jong-June Jeon, Hosik Choi
• Abstract summary: We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields explainable latent space by EXplainable encoder Network (EXoN) Negative cross-entropy and Kullback-Leibler divergence play a crucial role in constructing explainable latent space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields explainable latent space by EXplainable encoder Network (EXoN). The EXoN provides two useful tools for implementing VAE. First, we can freely assign a conceptual center of latent distribution for a specific label. We separate the latent space of VAE with multi-modal property of the Gaussian mixture distribution according to labels of observations. Next, we can easily investigate the latent subspace by a simple statistics, known as $F$-statistics, obtained from the EXoN. We found that both negative cross-entropy and Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'. With MNIST and CIFAR-10 dataset, we show that the EXoN can produce explainable latent space which effectively represents labels and characteristics of the images.

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