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
MS in Computer Science
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(2019).Generating large scale images using GANs [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Mohamed, Mohamed Mohsen Mahmoud. Generating large scale images using GANs. 2019. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.