Geometry of Deep Generative Models for Disentangled Representations

19 Feb 2019Ankita ShuklaShagun UppalSarthak BhagatSaket AnandPavan Turaga

Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an embedding function. The geometric properties of these latent spaces has been studied under the lens of Riemannian geometry; via analysis of the non-linearity of the generator function... (read more)

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