Abstract
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.
Department
Computer Science & Engineering Department
Degree Name
MS in Computer Science
Graduation Date
2-1-2019
Submission Date
January 2019
First Advisor
Moustafa, Mohamed
Committee Member 1
Goneid, Amr
Committee Member 2
Khalil, Mahmoud
Extent
045 p.
Document Type
Master's Thesis
Rights
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
Recommended Citation
APA Citation
Mohamed, M.
(2019).Generating large scale images using GANs [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/724
MLA Citation
Mohamed, Mohamed Mohsen Mahmoud. Generating large scale images using GANs. 2019. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/724