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A Systematic Review of Deep Dream | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 15, Volume 23, Issue 2, June 2023, Pages 192-209 PDF (1.47 M) | ||
| Document Type: Review Paper | ||
| DOI: 10.33103/uot.ijccce.23.2.15 | ||
| Authors | ||
| Lafta R. Al-Khazraji* 1; Ayad R. Abbas2; Abeer S. Jamil3 | ||
| 1Ph.D. Candidate, Computer Science Department, University of Technology, Baghdad, Iraq. Ministry of Education, General Directorate of Education in Salah Aldeen Governorate, Iraq. | ||
| 2Department of Computer Science, University of Technology, Iraq-Baghdad | ||
| 3Computer Technology Engineering Department, Al-Mansour University College, Baghdad, Iraq. | ||
| Abstract | ||
| Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely used in the artificial intelligence (AI) fields. This paper is the first systematic review of DD. We focused on the definition, importance, background, and applications of DD. Natural language processing (NLP), images, videos, and audio are the main fields in which DD is applied. We also discussed the main concepts of the DD, like transfer learning and Inception. We addressed the contributions, databases, and techniques that have been used to build the models, the limitations, and evaluation metrics for each one of the included research papers. Finally, some interesting recommendations have been listed to serve the researchers in the future. | ||
| Keywords | ||
| Deep dream; deep CNN; gradient ascent; Inception; style transfer | ||
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