Abstract: Painting is a popular form of art. Style transfer is the technique of recomposing images in the style of other images. In the past, it takes lots of time for a well-trained artist to redraw an image in a particular style. Recently, inspired by the power of Convolutional Neural Networks (CNNs), Gatys et al. published a frontier paper [1] on how it was actually possible to reproduce famous painting styles on natural images. The main idea behind their proposed algorithm is to iteratively optimize an image with the objective of matching desired CNN feature distribution, which involves both the artwork's style information and the photo's content information. With the appearance of a given artwork, their proposed algorithm successfully produces fantastic stylized images. The seminal work of Gatys et al. has attracted much attention from both industry and academia. In this brief note, we present some preliminary results on neural artistic style transfer for traditional Chinese painting

• Publication Date: 31-May-2020
• DOI: 10.15224/978-1-63248-188-7-21
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Abstract: This paper aims to investigate the relevance of job competency and Job performance with demographic factors for tourism managers in Mongolia.This quantitative study has constructed 61 questions from 331 respondents that challengethe current understanding of role and relevance tourism manager's Job competency on how to affect their job performance in the Mongolian tourism sector.The research adopted an exploratory Study design and an explanatory approach which involving regression and correlation analysis to explain predict the relationship and T-test, one way ANOVA approach is designed to obtain the difference between variables, using SPSS 20.0 and Smart PLS in this research.

• Publication Date: 31-May-2020
• DOI: 10.15224/978-1-63248-188-7-22
• Views: 0
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