PGST: A Persian gender style transfer method

Neural Style Transfer: A Review

The recent work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic fantastic imagery by separating and recombing the image content and style. This process of using CNN to migrate the semantic content of one image to different styles is referred to as Neural Style Transfer. Since then, Neural Style Transfer has become a trending topic both in acade...

Gender and Leadership Style: A Meta-Analysis

Gender Representation in Persian Folktales for Children

the present study was an attempt to add to the understanding of gendered content in Persian folklore stories for children. A deductive content analysis was performed to identify and record gender representation in titles, central roles, and the total number of characters. Traditionally feminine and masculine stereotypes were also investigated based on a coding scheme. The sample included 50 pop...

Stereoscopic Neural Style Transfer

This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR/VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original disparity consistency cannot be well preserved in the final stylization result...

Demystifying Neural Style Transfer

Neural Style Transfer [Gatys et al., 2016] has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation pro...