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Neural Computing and Applications
Published in: Neural Computing and Applications 9/2021

07-08-2020 | Original Article

Attribute-based regularization of latent spaces for variational auto-encoders

Authors: Ashis Pati, Alexander Lerch

Published in: Neural Computing and Applications | Issue 9/2021

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Abstract

Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a variational auto-encoder to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded. Consequently, post training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension. The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces which can be used to effectively manipulate a wide range of data attributes spanning image and symbolic music domains.
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Appendix
Available only for authorised users
Footnotes
2
https://​faceapp.​com/​app, last accessed: 20th July 2020.
 
3
https://​prisma-ai.​com, last accessed: 20th July 2020.
 
4
https://​pytorch.​org, last accessed: 20th July 2020.
 
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Metadata
Title
Attribute-based regularization of latent spaces for variational auto-encoders
Authors
Ashis Pati
Alexander Lerch
Publication date
07-08-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05270-2

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