Finding disentangled representations using VAE

Bachelor thesis (2021)

Authors

R. d'Anjou Electrical Engineering, Mathematics and Computer Science

Contributors

Stavros Makrodimitris Pattern Recognition and Bioinformatics - (mentor)

S. Makrodimitris Pattern Recognition and Bioinformatics - (mentor)

T.R.M. Abdelaal Pattern Recognition and Bioinformatics - (mentor)

Tamim Abdelaal Pattern Recognition and Bioinformatics - (mentor)

Mohammed Charrout Pattern Recognition and Bioinformatics - (mentor)

M. Charrout Pattern Recognition and Bioinformatics - (mentor)

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

Mostafa Eltager Pattern Recognition and Bioinformatics - (mentor)

E. Isufi (graduation committee member)

M. J.T. Reinders Pattern Recognition and Bioinformatics - (graduation committee member)

M.J.T. Reinders Pattern Recognition and Bioinformatics - (graduation committee member)

Marcel J.T. Reinders Pattern Recognition and Bioinformatics - (graduation committee member)

Marcel Reinders Pattern Recognition and Bioinformatics - (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

To reference this document use:

http://resolver.tudelft.nl/uuid:20f07c91-4485-44f7-9991-974c61d5df54

More Info

expand_more

Published Date

02-07-2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

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.

Files

FINAL_FINAL_FINAL_PAPER.pdf

(pdf | 0.697 Mb)

Unknown license