Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT ² ).

中文翻译：

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高效的样式-Corpus约束学习，实现逼真的样式转移

逼真的样式转移是一项艰巨的任务，需要样式化的图像保持真实。现有方法仍然遭受不切实际的伪像和沉重的计算成本。在本文中，我们提出了一种新颖的样式-Corpus约束学习（SCCL）方案来解决这些问题。提出了同时具有风格特定性和风格不可知性的风格语料库，以约束不同样本之间具有风格一致性的风格化图像，从而提高了风格化输出的真实感。通过使用对抗蒸馏学习策略，训练了一个简单的快速执行网络来替代以前的复杂特征转换模型，从而显着降低了计算成本。实验表明，我们的方法可生成细节丰富的逼真的图像， ² ）。