Abstract:
"
In the present Generative Adversarial Networks have performed remarkable results in image
translation and generation. Transfer learning technique called unsupervised domain adaptation
has also attained remarkable results when it comes to semantic segmentation tasks. However,
unsupervised domain adaptation techniques have recently worked with GAN models very well.
Latent space vectors are interpreted as inputs for the generator to generate images. We also
compare image translation and generative gan works and unsupervised domain adaptation
existing literature.
Based on the analysis, we choose the appropriate existing works to complete the research. We
choose Art2Real as the existing model and unsupervised domain adaptation as the model
optimization technique for improving the existing model to get a good outcome. The results of
the improved Art2Real model allowed us to get a high accuracy and quality realistic images of
the same artistic images.
The research on the image translation techniques allowed us to translate artistic images into
realistic photos using improved image generative models. While there are other image generative
and translation models, they aren’t successful to generate quality realistic images of the artistic
images. The author reviewed existing works on image generative models specially for the art
works to realistic images works and also the models which used unsupervised domain
adaptation.
"
