Sparse Latent Space Acquisition with Variational Autoencoders Based on Tsallis Statistics

Abstract

In machine learning, transforming features into a low-dimensional latent space has advantages such as speeding up learning and suppressing overfitting. In addition, when each feature in the latent space is independent and latent space is sparse, the overlap of information between features can be reduced. Such a sparse latent space can be useful for acquiring a policy of robot with high-dimensional sensors like a camera. To obtain the sparse latent space, a variational autoencoder based on Tsallis statistics is rearranged and analyzed. From vision information on a car racing simulation, the proposed method, which is with a more natural implementation than the previous method, can extract the sparse latent space appropriately.