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Generating large scale images using GANs
Author(s): Mohamed Mohsen; Mohamed Moustafa
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Paper Abstract

Generative Adversarial Networks (GANs) 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 to generate 128x128 pixel images and obtained better inception scores over the baseline GAN.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790N (14 August 2019); doi: 10.1117/12.2540489
Show Author Affiliations
Mohamed Mohsen, American Univ. in Cairo (Egypt)
Mohamed Moustafa, American Univ. in Cairo (Egypt)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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