Finding disentangled representations using VAE

Bachelor Thesis (2021)
Authors

R. d'Anjou (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Supervisors

S. Makrodimitris (TU Delft - Pattern Recognition and Bioinformatics)

Tamim R.M. Abdelaal (TU Delft - Pattern Recognition and Bioinformatics)

Mohammed Charrout (TU Delft - Pattern Recognition and Bioinformatics)

M.A.M.E. Eltager (TU Delft - Pattern Recognition and Bioinformatics)

Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Raymond d'Anjou
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Raymond d'Anjou
Graduation Date
02-07-2021
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Abstract

This study presents a comparison of different VariationalAutoencoder(VAE) models to see which VAE models arebetter at finding disentangled representations. Specificallytheir ability to encode biological processes into distinct la-tent dimensions. The biological processes that will be lookedat are the cell cycle and differentiation state. The cell cycleis expressed as a S- and G2M-Score and the differentiationstate is expressed as a number that quantifies the develop-ment time of the cells. First the models will be trained, afterthat the models will be evaluated. The evaluation is doneby checking the latent dimensions for a correlation with thetwo aforementioned biological processes. From this it be-came quite clear that VAE and DIP-VAE performed theworst out of the four models tested. On the other handβ-VAE andβ-TCVAE performed by far the best.

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