Variational Autoencoder with Tsallis Statistics for Disentangled Representation Learning

This paper proposes a novel variational autoencoder derived from Tsallis statistics, named q-VAE. Starting from the viewpoint of Tsallis statistics, a new lower bound of the q-VAE is derived to maximize likelihood of the data sampled, which has a potential for disentangled representation learning. As another advantage of the q-VAE, it does not require independency between the data. The q-VAE is demonstrated in learning latent dynamics of a nonlinear dynamical simulation. As a result, the q-VAE achieves stable and accurate long-term state prediction from the initial state and the actions at respective times.