Formal Linguistic Features, Mature Artistic Style—— A Stylistic Analysis of Joyce's A Portrait of the Artist as a Young Man

A Portrait of the Artist as a Young Man by James Joyce is a novel full of different linguistic varieties,making it hard for common readers to comprehend.Stylistic analysis provides a different but not difficult way for common readers to explore how and why the novelist employs such a foregrounding writing technique to depict its hero,develop its plot and sublime its theme.

APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models.Aiming to this,this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration.The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology,thereby the generalization performance and the convergence stability are improved.For the generator,a Flow-based network using Wavelet additive coupling(WAC)layers is implemented to extract multi-scale content features.Also,a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input images.To enhance the generated salient details,a loss of adaptive stroke edge is applied in both the global and local model training.The experimental results show that the proposed method improves PSNR by 5%,SSIM by 6.2%,and decreases Style Error by 29.4%over the existing models on the ChipPhi set.The competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization,thereby can be further applied to more APST scenes.

Stroke-GAN Painter:Learning to paint artworks using stroke-style generative adversarial networks

It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion.Despite advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for paintings.In this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two limitations.Stroke-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke styles.We design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted details.To overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of paintings.Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.