NDLI: Creative and diverse artwork generation using adversarial networks

Content Provider	IET Digital Library
Author	Chen, Haibo Zhao, Lei Qiu, Lihong Wang, Zhizhong Zhang, Huiming Xing, Wei Lu, Dongming
Abstract	Existing style transfer methods have achieved great success in artwork generation by transferring artistic styles onto everyday photographs while keeping their contents unchanged. Despite this success, these methods have one inherent limitation: they cannot produce newly created image contents, lacking creativity and flexibility. On the other hand, generative adversarial networks (GANs) can synthesise images with new content, whereas cannot specify the artistic style of these images. The authors consider combining style transfer with convolutional GANs to generate more creative and diverse artworks. Instead of simply concatenating these two networks: the first for synthesising new content and the second for transferring artistic styles, which is inefficient and inconvenient, they design an end-to-end network called ArtistGAN to perform these two operations at the same time and achieve visually better results. Moreover, to generate images of higher quality, they propose the bi-discriminator GAN containing a pixel discriminator and a feature discriminator that constrain the generated image from pixel level and feature level, respectively. They conduct extensive experiments and comparisons to evaluate their methods quantitatively and qualitatively. The experimental results verify the effectiveness of their methods.
Starting Page	650
Ending Page	657
Page Count	8
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 8, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2020.0014
Journal	IET Computer Vision
Publisher Date	2020-10-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Art ArtistGAN Artistic Style Bidiscriminator GAN Computer Vision And Image Processing Technique Convolutional GAN Convolutional Generative Adversarial Network Convolutional Neural Nets Creative Artwork Generation Diverse Artwork Generation End-to-end Network Feature Discriminator Humanities Computing Image Generation Image Texture Neural Computing Technique Optical, Image And Video Signal Processing PhotoGraph Photography Pixel Discriminator Style Transfer Method Unsupervised Learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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