關鍵字查詢 | 類別:期刊論文 | | 關鍵字:A Study on the Convolutional Neural Algorithm of Image Style Transfer

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序號 學年期 教師動態
1 107/1 資工系 林慧珍 教授 期刊論文 發佈 A study on the convolutional neural algorithm of image style transfer , [107-1] :A study on the convolutional neural algorithm of image style transfer期刊論文A study on the convolutional neural algorithm of image style transferFu Wen Yang; Hwei Jen Lin; Shwu-Huey Yen; Chun-Hui WangMax-pooling;back-propagation;style transfer;overfitting;deep convolutional neural networks;fully convolutional networks;receptive field;kernel;merge kernelInternational Journal of Pattern Recognition and Artificial Intelligence 33(5), P.1954020Recently, deep convolutional neural networks have resulted in noticeable improvements in image classification and have been used to transfer artistic style of images. L. A. Gatys et al. proposed the use of a learned CNN (Convolutional Neural Network) 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 efficient
2 107/1 資工系 顏淑惠 教授 期刊論文 發佈 A Study on the Convolutional Neural Algorithm of Image Style Transfer , [107-1] :A Study on the Convolutional Neural Algorithm of Image Style Transfer期刊論文A Study on the Convolutional Neural Algorithm of Image Style TransferFu Wen Yang; Hwei Jen Lin; Shwu-Huey Yen; Chun-Hui WangMax-pooling;back-propagation;style transfer;over-fitting;deep convolutional neural networks;fully convolutional networks;receptive field;kernel;merge kernelInternational Journal of Pattern Recognition and Artificial Intelligence 33(5), p.1954020Recently, 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 ch
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