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An Improved Image Transformation Network for Neural Style Transfer

Abstract : By using Convolutional Neural Networks (CNNs), the semantics and styles of images can be separated and recombined to create fascinating images. In this paper, an image transformation network for style transfer is proposed, which consists of convolution layers, deconvolution layers and Fusion modules composed of two 1 × 1 convolution layers and a residual block. The output of each layer in the network is normalized using batch normalization to speed up the training process. Compared with other networks, our network has fewer parameters and better real-time performance while generating similar quality images.
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Submitted on : Friday, June 22, 2018 - 10:43:29 AM
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Hong-Xu Qiu, Xiao-Xia Huang. An Improved Image Transformation Network for Neural Style Transfer. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.260-267, ⟨10.1007/978-3-319-68121-4_28⟩. ⟨hal-01820915⟩



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