期刊論文
| 學年 | 107 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2019-03-04 |
| 作品名稱 | A Study on the Convolutional Neural Algorithm of Image Style Transfer |
| 作品名稱（其他語言） | |
| 著者 | Fu Wen Yang; Hwei Jen Lin; Shwu-Huey Yen; Chun-Hui Wang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | International Journal of Pattern Recognition and Artificial Intelligence 33(5), p.1954020 |
| 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. |
| 關鍵字 | Max-pooling;back-propagation;style transfer;over-fitting;deep convolutional neural networks;fully convolutional networks;receptive field;kernel;merge kernel |
| 語言 | en_US |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116071 ) |