Solving the reconstruction-generation trade-off: Generative model with implicit embedding learning

Summary: This paper proposes a method to learn minimizing geodesics within a data manifold for generating high-quality uniform interpolations. By using an autoencoder network to map data samples into a latent space and adding prior geometric information, the method is able to achieve this goal effectively. Theoretical analysis and the example of image interpolation demonstrate the effectiveness of the proposed method.