淡江大學機構典藏:Item 987654321/116071
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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116071


    Title: A Study on the Convolutional Neural Algorithm of Image Style Transfer
    Authors: Yang, Fu Wen;Lin, Hwei Jen;Yen, Shwu-Huey;Wang, Chun-Hui
    Keywords: Max-pooling;back-propagation;style transfer;over-fitting;deep convolutional neural networks;fully convolutional networks;receptive field;kernel;merge kernel
    Date: 2018-11-05
    Issue Date: 2019-03-30 12:10:48 (UTC+8)
    Abstract: Recently, deep convolutional neural networks have resulted in noticeable improvements in image classification and have been used to transfer artistic style of images. Gatys et al. proposed the use of a learned Convolutional Neural Network (CNN) architecture VGG to transfer image style, but problems occur during the back propagation process because there is a heavy computational load. This paper solves these problems, including the simplification of the computation of chains of derivatives, accelerating the computation of adjustments, and efficiently choosing weights for different energy functions. The experimental results show that the proposed solutions improve the computational efficiency and render the adjustment of weights for energy functions easier.
    Relation: International Journal of Pattern Recognition and Artificial Intelligence 33(5), p.1954020
    DOI: 10.1142/S021800141954020X
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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