Title: On the Transformation of Latent Space in Autoencoders,
Abstract: Noting the importance of the latent variables in inference and learning, we
propose a novel framework for autoencoders based on the homeomorphic
transformation of latent variables --- which could reduce the distance between
vectors in the transformed space, while preserving the topological properties
of the original space --- and investigate the effect of the transformation in
both learning generative models and denoising corrupted data. The results of
our experiments show that the proposed model can work as both a generative
model and a denoising model with improved performance due to the transformation
compared to conventional variational and denoising autoencoders.