  Learning interpretable and interpolatable latent representations has been an
emerging research direction, allowing researchers to understand and utilize the
derived latent space for further applications such as visual synthesis or
recognition. While most existing approaches derive an interpolatable latent
space and induces smooth transition in image appearance, it is still not clear
how to observe desirable representations which would contain semantic
information of interest. In this paper, we aim to learn meaningful
representations and simultaneously perform semantic-oriented and
visually-smooth interpolation. To this end, we propose an angular
triplet-neighbor loss (ATNL) that enables learning a latent representation
whose distribution matches the semantic information of interest. With the
latent space guided by ATNL, we further utilize spherical semantic
interpolation for generating semantic warping of images, allowing synthesis of
desirable visual data. Experiments on MNIST and CMU Multi-PIE datasets
qualitatively and quantitatively verify the effectiveness of our method.
