دانلود مقاله انگلیسی رایگان:AI Illustrator: تولید تصویر هنری بر اساس شبکه تخاصمی تولیدی - 2020
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  • AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network
    AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network


    ترجمه فارسی عنوان مقاله:

    AI Illustrator: تولید تصویر هنری بر اساس شبکه تخاصمی تولیدی


    منبع:

    IEEE - 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC);2020; ; ;


    نویسنده:

    Zihan Chen1,*, Lianghong Chen1,a, Zhiyuan Zhao1,b, Yue Wang


    چکیده انگلیسی:

    In recent years, peoples pursuit of art has been on the rise. People want computers to be able to create artistic paintings based on descriptions. In this paper, we proposed a novel project, Painting Creator, which uses deep learning technology to enable the computer to generate artistic illustrations from a short piece of text. Our scheme includes two models, image generation model and style transfer model. In the real image generation model, inspired by the application of stack generative adversarial networks in text to image generation, we proposed an improved model, IStackGAN, to solve the problem of image generation. We added a classifier based on the original model and added image structure loss and feature extraction loss to improve the performance of the generator. The generator network can get additional hidden information from the classification information to produce better pictures. The loss of image structure can force the generator to restore the real image, and the loss of feature extraction can verify whether the generator network has extracted the features of the real image set. For the style transfer model, we improved the generator based on the original cycle generative adversarial networks and used the residual block to improve the stability and performance of the u-net generator. To improve the performance of the generator, we also added the cycle consistent loss with MS-SSIM. The experimental results show that our model is improved significantly based on the original paper, and the generated pictures are more vivid in detail, and pictures after the style transfer are more artistic to watch.
    Keywords : Image generation | style transfer


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 5
    حجم فایل: 521 کیلوبایت

    قیمت: رایگان


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