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Learning interpretable and interpolatable latent representations has been anemerging research direction, allowing researchers to understand and utilize thederived latent space for further applications such as visual synthesis orrecognition. While most existing approaches derive an interpolatable latentspace and induces smooth transition in image appearance, it is still not clearhow to observe desirable representations which would contain semanticinformation of interest. In this paper, we aim to learn meaningfulrepresentations and simultaneously perform semantic-oriented andvisually-smooth interpolation. To this end, we propose an angulartriplet-neighbor loss (ATNL) that enables learning a latent representationwhose distribution matches the semantic information of interest. With thelatent space guided by ATNL, we further utilize spherical semanticinterpolation for generating semantic warping of images, allowing synthesis ofdesirable visual data. Experiments on MNIST and CMU Multi-PIE datasetsqualitatively and quantitatively verify the effectiveness of our method.

Semantics-Guided Representation Learning with Applications to Visual Synthesis