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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2021

19.07.2021 | Original Article

Visualization-based disentanglement of latent space

verfasst von: Runze Huang, Qianying Zheng, Haifang Zhou

Erschienen in: Neural Computing and Applications | Ausgabe 23/2021

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Abstract

In recent years, selecting manipulation of data attributes by changing latent code using auto-encoder has received considerable scholarly attention . However, the representation of the data encoded by the auto-encoder cannot be visually observed. Furthermore, the attribute values and the latent code of the dimension do not conform to a linear monotonic relationship. From a practical point of view, we propose a novel method that uses the encoder–decoder architecture to disentangle data into two visualizable representations that are encoded as latent spaces. Consequently, the encoded latent space can be used to manipulate data attributes in a simple and intuitive way. The experiments on image dataset and music dataset show that the proposed approach leads to produce complete interpretable latent spaces, which can be used to manipulate a wide range of data attributes and to generate realistic music via analogy.
Vorheriger Artikel CondenseNet with exclusive lasso regularization
Nächster Artikel Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

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Metadaten
Titel
Visualization-based disentanglement of latent space
verfasst von
Runze Huang
Qianying Zheng
Haifang Zhou
Publikationsdatum
19.07.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06223-z

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