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Presentation
Critical Visual Theory of the Latent Space
The thesis concentrates mostly on the second part, exploring different avenues for understanding the space. Using a multitude of vision generative models, it discusses possibilities for the systematic exploration of the space, including disentanglement properties and coverage of various guidance methods.
It also explores the possibility of mapping across latent spaces, and investigates the differences and commonalities across different learning experiments. Furthermore, the thesis investigates the role of stochasticity in newer models.
As a case study, this thesis adopts art historical data, spanning classic art, photography, and modern and contemporary art.
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