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Improving the Neural Algorithm of Artistic Style
[article]
2016
arXiv
pre-print
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In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style (by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge, arXiv:1508.06576). ...
In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer. ...
The suggested synthesizing algorithm generally produces great results in transferring repetitive artistic styles. ...
arXiv:1605.04603v1
fatcat:btrhvha6vvbu7bofarxg3ghvca
Towards the Algorithmic Detection of Artistic Style
2019
International Journal of Advanced Computer Science and Applications
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The artistic style of a painting can be sensed by the average observer, but algorithmically detecting a painting's style is a difficult problem. ...
We propose a novel method for detecting the artistic style of a painting that is motivated by the neuralstyle algorithm of Gatys et al. and is competitive with other recent algorithmic approaches to artistic ...
ACKNOWLEDGMENTS The author would like to thank NVIDIA for GPU donation to support this research, Wikiart.org for providing many of the images, the website Kaggle.com for hosting the data, and Kiri Nichols ...
doi:10.14569/ijacsa.2019.0100109
fatcat:zrt755d7y5g53ibm27skntnteu
Neural Style Representations and the Large-Scale Classification of Artistic Style
[article]
2016
arXiv
pre-print
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The recently introduced 'neural-style' algorithm substantially succeeds in merging the perceived artistic style of one image or set of images with the perceived content of another. ...
In light of this and other recent developments in image analysis via convolutional neural networks, we investigate the effectiveness of a 'neural-style' representation for classifying the artistic style ...
Acknowledgments The author would like to thank NVIDIA for GPU donation to support this research, Wikiart.org for providing many of the images, the website Kaggle.com for hosting the data, and Kiri Nichols ...
arXiv:1611.05368v1
fatcat:6cx3n7z46zennovqbrgsq5r2am
Interactive Neural Style Transfer with Artists
[article]
2020
arXiv
pre-print
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We gather a set of paired painting-pictures images and present a new evaluation methodology based on the predictivity of neural style transfer algorithms. ...
We point some algorithms' instabilities and show that they can be used to enlarge the diversity and pleasing oddity of the images synthesized by the numerous existing neural style transfer algorithms. ...
her own painting style, as well as pointing us to the series of Monet; Thibault Séjourné for helpful discussion on Optimal Transport; Vivien Cabannes and John Zarka for rereading and Stephane Mallat for ...
arXiv:2003.06659v1
fatcat:ghft7wnj2bhkdetiyjed4ur5w4
A Pragmatic AI Approach to Creating Artistic Visual Variations by Neural Style Transfer
[article]
2018
arXiv
pre-print
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To this aim, the present work explores a well-documented neural style transfer algorithm (Johnson 2016) in four experiments on four relevant visual parameters: number of iterations, learning rate, total ...
This paper explores the practicality of applying neural style transfer as an emerging design tool for generating creative digital content. ...
It is implemented in torch (Collobert et al. 2018 ) and provides an improved version of the neural style transfer algorithm in the Github repository "neural-style" (Johnson 2015) . ...
arXiv:1805.10852v1
fatcat:bsd563od2zbzvaaubp77bxchta
Robust Nonparametric Distribution Transfer with Exposure Correction for Image Neural Style Transfer
2020
Sensors
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Image neural style transfer is a process of utilizing convolutional neural networks to render a content image based on a style image. ...
The algorithm can compute a stylized image with original content from the given content image but a new style from the given style image. ...
Based on the analysis, we propose two neural style transfer models to ease the limitations and improve the effectiveness of the neural style transfer models. ...
doi:10.3390/s20185232
pmid:32937788
fatcat:5yae6yv6gfdx5byzouvlpj5vfe
Digital Image Art Style Transfer Algorithm Based on CycleGAN
2022
Computational Intelligence and Neuroscience
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In order to enhance the effect of image artistic style transfer, the image is recognized by using a multi-scale discriminator. ...
This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. ...
improve the overall effect of artistic style fusion and migration. ...
doi:10.1155/2022/6075398
pmid:35069722
pmcid:PMC8776490
fatcat:jvlhg3tornaw7cib4onchyrmku
Art Image Processing and Color Objective Evaluation Based on Multicolor Space Convolutional Neural Network
2021
Computational Intelligence and Neuroscience
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Considering the relationship between the evolution of artistic painting style and the color of artistic images, this article explores the characteristics of artistic image dimensions. ...
The model can directly input the original image, without the process of feature extraction and data reconstruction in common classification algorithms. ...
Acknowledgments is work was supported by the Hubei Institute of Fine Arts. ...
doi:10.1155/2021/4273963
fatcat:nbizdbbm55cczot52tioeyh6dy
A temporally coherent neural algorithm for artistic style transfer
2016
2016 23rd International Conference on Pattern Recognition (ICPR)
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Finally, I would like to acknowledge NVIDIA for donating the GPUs utilized in making this research possible. iv Abstract A Temporally Coherent Neural Algorithm for Artistic Style Transfer Michael Dushkoff ...
Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-byframe animations. ...
This later became the basis of a new neural algorithm that utilizes the same abstraction capabilities of CNNs to produce new works of art given only an example artistic style [29] . ...
doi:10.1109/icpr.2016.7900142
dblp:conf/icpr/DushkoffMP16
fatcat:tki3zo4l3fgado5uny4saexabe
An Improved Image Style Transfer Algorithm Based on Deep Learning Network
2019
DEStech Transactions on Computer Science and Engineering
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The experimental results show that the proposed algorithm can effectively improve the distortion of local style migration and provide theoretical support for the implementation of style migration technology ...
Aiming at the problem of local style migration distortion in image stylization, an improved image style migration algorithm based on deep learning network is proposed. ...
Acknowledgement This research was financially supported by the National Natural Science Foundation and Joint Training Base of Postgraduate Education in Shandong Province. ...
doi:10.12783/dtcse/cscme2019/32548
fatcat:ko4m643j45fv5joha4lgjj2voe
The Intra-Class and Inter-Class Relationships in Style Transfer
2018
Applied Sciences
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semantic content of one image to different artistic styles by using convolutional neural network (CNN). ...
After careful analysis of some different algorithms for style loss construction, we discovered that some algorithms consider the relationships between different feature maps of a layer obtained from the ...
Demystifying Neural Style Transfer Although all the methods mentioned above have improved over the original neural style transfer significantly, they all use the same Gram matrix as the original algorithm ...
doi:10.3390/app8091681
fatcat:awv4yuv7wnef5idl3gxfqzobae
Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art
[article]
2017
arXiv
pre-print
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Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve. ...
In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic ...
The work was supported by grants of the Romanian National Authority for Scientific Research and Innovation, CNCS UEFISCDI, number PN-II-RU-TE-2014-4-0733 and respectively, CCCDI-UEFISCDI, project number ...
arXiv:1712.03727v1
fatcat:64iht2a5ubbntkmuvkxfsdjtuy
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies
[article]
2016
arXiv
pre-print
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First, they use convolutional neural network features to build a statistical model for the style of an image. ...
Then they create a new image with the content of one image but the style statistics of another image. Here, we extend this method to render a movie in a given artistic style. ...
In conclusion, our work demonstrates that we can improve the temporal coherence of an artistically styled movie by making use of optical flow. ...
arXiv:1605.08153v1
fatcat:femlb6nek5atdlcqbnv74le6aq
Neural style transfer
2017
Proceedings of the Symposium on Non-Photorealistic Animation and Rendering - NPAR '17
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ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their comments and suggestions on how to improve the paper. ...
This work was partly funded by the Federal Ministry of Education and Research (BMBF), Germany, for the AVA project 01IS15041B. ...
the learning phase for improving a style transfer. ...
doi:10.1145/3092919.3092920
dblp:conf/npar/SemmoID17
fatcat:3udzwr4mcndozpawlr2q34b47i
Deep Convolutional Nets Learning Classification for Artistic Style Transfer
2022
Scientific Programming
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The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which ...
Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. ...
layers
Related Work e style images and the recombination of the images are separated by creating the artistic images with high quality using neural representations from the neural algorithm; this utilizes ...
doi:10.1155/2022/2038740
fatcat:as5sycc6wfdjlhgkibhg3hneue
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