Unbiased Image Style Transfer
Hyun-Chul Choi, Minseong Kim

TL;DR
This paper analyzes the effectiveness of style strength interpolation in neural style transfer and proposes an unbiased training method that improves control over style strength and stability of the output.
Contribution
It introduces an unbiased learning technique for style transfer networks, enabling better style control and more stable, accurate style interpolation.
Findings
Improved style control with unbiased training
Content image reconstruction at zero style strength
Enhanced stability of style transfer results
Abstract
Recent fast image style transferring methods use feed-forward neural networks to generate an output image of desired style strength from the input pair of a content and a target style image. In the existing methods, the image of intermediate style between the content and the target style is obtained by decoding a linearly interpolated feature in encoded feature space. However, there has been no work on analyzing the effectiveness of this kind of style strength interpolation so far. In this paper, we tackle the missing work on the in-depth analysis of style interpolation and propose a method that is more effective in controlling style strength. We interpret the training task of a style transfer network as a regression learning between the control parameter and output style strength. In this understanding, the existing methods are biased due to the fact that training is performed with…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques · Image Enhancement Techniques
