English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 56557/90363 (63%)
造訪人次 : 11846998      線上人數 : 114
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116071

    題名: A Study on the Convolutional Neural Algorithm of Image Style Transfer
    作者: Yang, Fu Wen;Lin, Hwei Jen;Yen, Shwu-Huey;Wang, Chun-Hui
    關鍵詞: Max-pooling;back-propagation;style transfer;over-fitting;deep convolutional neural networks;fully convolutional networks;receptive field;kernel;merge kernel
    日期: 2018-11-05
    上傳時間: 2019-03-30 12:10:48 (UTC+8)
    摘要: 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.
    關聯: International Journal of Pattern Recognition and Artificial Intelligence 33(5), p.1954020
    DOI: 10.1142/S021800141954020X
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋