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Low Complexity Data-Efficient Generation in Disentangled Setting
Shavit Borisov and Jacob Sela
Supervised by Elad Richardson
Recent advancements in the fields of generation are promising better results, with more control over generation. Unfortunately, these results are achieved with massive data sets used to train highly complex models by the leading experts of machine learning, making them inaccessible to "the average Joe". In this paper, we propose that the disentangled latent spaces created as a by-product of these tools can be repurposed for generation with a specific factor of variation in mind, using simple tools and little data. We demonstrate these claims by generating aging videos using NVidia StyleGAN’s latent space from a single source image.
Please, see project report.
Please, see final presentation.