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Exploiting the deep generative model's remarkable ability of learning thedata-manifold structure, some recent researches proposed a geometric datainterpolation method based on the geodesic curves on the learned data-manifold.However, this interpolation method often gives poor results due to atopological difference between the model and the dataset. The model defines afamily of simply-connected manifolds, whereas the dataset generally containsdisconnected regions or holes that make them non-simply-connected. Tocompensate this difference, we propose a novel density regularizer that makethe interpolation path circumvent the holes denoted by low probability density.We confirm that our method gives consistently better interpolation results fromthe experiments with real-world image datasets.

Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology