| Summary: | 碩士 === 淡江大學 === 資訊工程學系資訊網路與多媒體碩士班 === 104 === Recently, deep convolutional neural networks have resulted in noticeable improvements in image classification and are used to transfer artistic style of images. L. A. Gatys et al. [6] proposed the use of a learned CNN (Convolutional Neural Network) architecture VGG [16] 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.
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