EXoN: EXplainable encoder Network

Abstract

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.

References in corpus (11)

• Categorical Reparameterization with Gumbel-Softmax
• Semi-Supervised Learning with Deep Generative Models
• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
• Differentiable Augmentation for Data-Efficient GAN Training
• Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
• Topic-Guided Variational Autoencoders for Text Generation
• Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
• Preventing Posterior Collapse with delta-VAEs
• Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
• Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders