Master Thesis MSTR-2021-71

BibliographyMaalouly, Jad: Deep learning for scatterplot similarity.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 71 (2021).
48 pages, english.

Through the technology of generative models, one can now learn a distribution of his input data and generate new and similar output where it mimics the behavior of his input. Variational Auto-Encoders (VAE) are generative models where the neural network tries to learn a distribution of the latent vector I. Disentanglement using VAE’s tries to decouple the latent vector and render each dimension independent from the other. Using disentanglement we wish to learn the three basic visual properties size, shape, and color. All the different disentangled models used in this paper aim to refine the reconstruction and disentangled representation. After training our models the user will have control over the three properties. Next, wish to create a tool where the user can control the structural properties of a scatterplot. By simply training a disentangled model, we would like to gain control over the different structures of a scatterplot to generate similar data. Furthermore, we will be discussing the limitations and shortcomings of using such models.

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Department(s)University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
Superviser(s)Sedlmair, Prof. Michael; Cutura, Rene; Moralu, Cristina
Entry dateFebruary 15, 2022
   Publ. Computer Science