The program made in this thesis, has a goal that is to do car attribute editing
via latent vector exploration. The attribute that will be explored is the ambience of
an image. After the latent space exploration is finished, the resulting latent vectors
that are with more than 1 ambience will be saved and then shown in 2D data
distribution using the Principal Component Analysis (PCA) method. The final
product of this project is Python programs. Because of the time needed to do the
exploration and save the latent vector the program is divided into 2 parts. First is
the latent vector explorer and the second one is the program to show the saved latent
vectors in terms of a 2D distribution.
To achieve the goal of this study, this program used an unsupervised
approach to generate images using Generative Adversarial Network and one of its
extended versions called StyleGAN. Current techniques of unsupervised
Generative Adversarial Network require a lot of high quality images to train the
network and do the attribute editing. Because of that, in this thesis, a customized
stanford cars dataset is used. The customization is done by adding ambiences in
the training images. The evaluation of the exploration is done manually. For a better
visualization of the resulting latent vectors, it is done in a 2D distribution of points.
Even though the training and exploration took so much time, the StyleGAN
technique produces convincing qualitative results from the dataset. After training
and exploration, because the generating process of 1 image only takes a few
seconds, it is possible to make interactive user-interface applications that show the
saved latent vectors.

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