Generative Adversarial Networks have been used for the task of image generation and has achieved impressive results. There is always a challenge to train networks that generate large scale images since they tend to be huge and training needs a lot of data. In this work, we tackle this problem by dividing it into two smaller parts. We first generate small scale images using GANs then use a super resolution network to enlarge the generated images resulting in large scale images. Using a super resolution network helps in adding more details to the image which results in a better quality image. This technique has been tested with a small amount of data and obtained better inception scores over the baseline GAN.


Computer Science & Engineering Department

Degree Name

MS in Computer Science

Graduation Date


Submission Date

January 2019

First Advisor

Moustafa, Mohamed

Committee Member 1

Goneid, Amr

Committee Member 2

Khalil, Mahmoud


045 p.

Document Type

Master's Thesis


The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy.

Institutional Review Board (IRB) Approval

Not necessary for this item