Employing Neural Style Transfer for Generating Deep Dream Images

Al-Khazraji, Lafta R. and Abbas, Ayad R. and Jamil, Abeer S. (2022) Employing Neural Style Transfer for Generating Deep Dream Images. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10 (2). pp. 134-141. ISSN 2410-9355

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Abstract

In recent years, deep dream and neural style transfer emerged as hot topics in deep learning. Hence, mixing those two techniques support the art and enhance the images that simulate hallucinations among psychiatric patients and drug addicts. In this study, our model combines deep dream and neural style transfer (NST) to produce a new image that combines the two technologies. VGG-19 and Inception v3 pre-trained networks are used for NST and deep dream, respectively. Gram matrix is a vital process for style transfer. The loss is minimized in style transfer while maximized in a deep dream using gradient descent for the first case and gradient ascent for the second. We found that different image produces different loss values depending on the degree of clarity of that images. Distorted images have higher loss values in NST and lower loss values with deep dreams. The opposite happened for the clear images that did not contain mixed lines, circles, colors, or other shapes.

Item Type:	Article
Uncontrolled Keywords:	Deep dream, Gradient ascent, Gram matrix, Neural style transfer
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	ARO-The Scientific Journal of Koya University > VOL 10, NO 2 (2022)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	15 Dec 2022 08:36
Last Modified:	15 Dec 2022 08:36
URI:	http://eprints.koyauniversity.org/id/eprint/345

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