Disentangled Representation Learning and Generation with Manifold Optimization

Pandey, Arun; Fanuel, Michaël; Schreurs, Joachim; Suykens, Johan A. K.

Document type :

Pré-publication ou Document de travail

Title :

Disentangled Representation Learning and Generation with Manifold Optimization

Author(s) :

Pandey, Arun [Auteur]
Fanuel, Michaël [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Schreurs, Joachim [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Suykens, Johan A. K. [Auteur]

HAL domain(s) :

Sciences de l'ingénieur [physics]
Mathématiques [math]
Informatique [cs]

English abstract : [en]

Disentanglement is a useful property in representation learning which increases the interpretability of generative models such as Variational Auto-Encoders (VAE), Generative Adversarial Models, and their many variants. ...
Show more >Disentanglement is a useful property in representation learning which increases the interpretability of generative models such as Variational Auto-Encoders (VAE), Generative Adversarial Models, and their many variants. Typically in such models, an increase in disentanglement performance is traded-off with generation quality. In the context of latent space models, this work presents a representation learning framework that explicitly promotes disentanglement by encouraging orthogonal directions of variations. The proposed objective is the sum of an auto-encoder error term along with a Principal Component Analysis reconstruction error in the feature space. This has an interpretation of a Restricted Kernel Machine with the eigenvector matrix valued on the Stiefel manifold. Our analysis shows that such a construction promotes disentanglement by matching the principal directions in the latent space with the directions of orthogonal variation in data space. In an alternating minimization scheme, we use Cayley ADAM algorithm -- a stochastic optimization method on the Stiefel manifold along with the ADAM optimizer. Our theoretical discussion and various experiments show that the proposed model improves over many VAE variants in terms of both generation quality and disentangled representation learning.Show less >

Language :

Anglais

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

http://arxiv.org/pdf/2006.07046
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2006.07046
Open access
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