Unbiased Decoder Learning for Fast Image Style Transfer
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Image style transfer is one of the computer vision applications related to deep machine learning. Since the proposal of the first online learning approach of single layered neural network called neural style, image style transferring method has been continuously improved in processing speed and style capacity. However, controlling the style strength of image has not been investigated deeply. As an early stage of research for style strength control, we propose a method of style manifold learning in image decoder which can generate unbiased style image for image style transfer.
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