Understanding latent vector arithmetic for attribute manipulation in normalizing flows

Gimenez Funes, Eduard

Understanding latent vector arithmetic for attribute manipulation in normalizing flows

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Gimenez Funes, E. Understanding latent vector arithmetic for attribute manipulation in normalizing flows. 2021. handle: http://hdl.handle.net/10230/49203

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Abstract

Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is defined to Rn is such that the final distribution is a Gaussian. In “Glow: Generative flow with invertible 1x1 convolutions” Kingma et al introduced the Glow model. Normalizing flows arrange the latent space in such a way that feature additivity is possible, allowing synthetic image generation. For example, it is possible to take the image of a person not smiling, add a smile, and obtain the image of the same person smiling. Using the CelebA dataset we report new experimental properties of the latent space such as specular images and linear discrimination. Finally, we propose a mathematical framework that helps to understand why feature additivity works.

Description

Treball fi de màster de: Master's Degree in Data Science. Curs 2020-2021
Directors: Vicenç Gómez (UPF) , Carlos Segura and Ferran Diego (Telefónica Research)