Print Email Facebook Twitter Finding disentangled representations using VAE Title Finding disentangled representations using VAE Author d'Anjou, Raymond (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Makrodimitris, S. (mentor) Abdelaal, T.R.M. (mentor) Charrout, M. (mentor) Eltager, M.A.M.E. (mentor) Isufi, E. (graduation committee) Reinders, M.J.T. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-02 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. Subject VAEDeep learningBiological processes To reference this document use: http://resolver.tudelft.nl/uuid:20f07c91-4485-44f7-9991-974c61d5df54 Part of collection Student theses Document type bachelor thesis Rights © 2021 Raymond d'Anjou Files PDF FINAL_FINAL_FINAL_PAPER.pdf 713.23 KB Close viewer /islandora/object/uuid:20f07c91-4485-44f7-9991-974c61d5df54/datastream/OBJ/view