
Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.

Arbitrary Style Transfer with Parallel Self-Attention

PS T5.3: Imaging and Deep Image Processing

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Tiange Zhang

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Arbitrary Style Transfer with Parallel Self-Attention

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