Finding disentangled representations using VAE
R. d'Anjou (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Makrodimitris – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
T. Abdelaal – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
M. Charrout – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
M.A.M.E. Eltager – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
E. Isufi – Graduation committee member (TU Delft - Multimedia Computing)
MJT Reinders – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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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.