Latent Space Factorisation and Manipulation via Matrix Subspace Projection

Xiao Li, Chenghua Lin, Chaozheng Wang, Frank Guerin

Research output: Working paper

Abstract

This paper proposes a novel method for factorising the information in the latent space of an autoencoder (AE), to improve the interpretability of the latent space and facilitate controlled generation. When trained on a dataset with labelled attributes we can produce a latent vector which separates information encoding the attributes from other characteristic information, and also disentangles the attribute information. This then allows us to manipulate each attribute of the latent representation individually without affecting others. Our method, matrix subspace projection, is simpler than the state of the art adversarial network approaches to latent space factorisation. We demonstrate the utility of the method for attribute manipulation tasks on the CelebA image dataset and the E2E text corpus.
Original languageEnglish
PublisherArXiv
Number of pages10
Publication statusSubmitted - 26 Jul 2019

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