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Generating large scale images using GANs
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 div...
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| Format: | Thesis |
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AUC Knowledge Fountain
2019
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| _version_ | 1867613411868672000 |
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| access_status_str | Open Access |
| author | Mohamed, Mohamed Mohsen Mahmoud |
| author_browse | Mohamed, Mohamed Mohsen Mahmoud |
| author_facet | Mohamed, Mohamed Mohsen Mahmoud |
| author_sort | Mohamed, Mohamed Mohsen Mahmoud |
| collection | Thesis |
| dc_rights_str_mv | 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. |
| description | 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. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-1723 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:43.583Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-1723 Generating large scale images using GANs Mohamed, Mohamed Mohsen Mahmoud 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. 2019-02-01T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/724 https://fount.aucegypt.edu/context/etds/article/1723/viewcontent/Mohamed_Mohsen_Thesis_Generating_Large_Scale_Images.pdf 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. Theses and Dissertations AUC Knowledge Fountain Image Generation Neural Networks |
| spellingShingle | Image Generation Neural Networks Mohamed, Mohamed Mohsen Mahmoud Generating large scale images using GANs |
| title | Generating large scale images using GANs |
| title_full | Generating large scale images using GANs |
| title_fullStr | Generating large scale images using GANs |
| title_full_unstemmed | Generating large scale images using GANs |
| title_short | Generating large scale images using GANs |
| title_sort | generating large scale images using gans |
| topic | Image Generation Neural Networks |
| url | https://fount.aucegypt.edu/etds/724 https://fount.aucegypt.edu/context/etds/article/1723/viewcontent/Mohamed_Mohsen_Thesis_Generating_Large_Scale_Images.pdf |
| work_keys_str_mv | AT mohamedmohamedmohsenmahmoud generatinglargescaleimagesusinggans |