An adversarial neuro-tensorial approach for learning disentangled representations
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data, while the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label information is not available. In this paper, we propose a pseudo-supervised deep learning method for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where the multiplicative interactions of multiple latent factors of variation are explicitly modelled by means of multilinear (tensor) structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.
Date Issued
2019-06-01
Date Acceptance
2019-02-04
Citation
International Journal of Computer Vision, 2019, 127 (6-7), pp.743-762
ISSN
0920-5691
Publisher
Springer
Start Page
743
End Page
762
Journal / Book Title
International Journal of Computer Vision
Volume
127
Issue
6-7
Copyright Statement
© 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/S010203/1
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Adversarial autoencoder
Disentangled representation
Tensor decomposition
DATABASE
Artificial Intelligence & Image Processing
0801 Artificial Intelligence and Image Processing
Publication Status
Published
Date Publish Online
2019-02-16