论文标题
量化：量化图像样式转移到高视觉保真度
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
论文作者
论文摘要
现有样式转移算法的机制是将混合损失函数最小化，以将生成的图像推向内容和样式的高相似性。但是，这种类型的方法不能保证视觉保真度，即，生成的艺术品应与真实的艺术品没有区别。在本文中，我们设计了一个名为Quantart的新样式转移框架，用于高视觉前景风格。 QuantArt将生成艺术品的潜在代表推向了使用矢量量化的真实艺术品分布的质心。通过融合量化和连续的潜在表示，Quantart可以在内容保存，样式相似性和视觉保真度方面对生成的艺术品进行灵活的控制。各种样式传输设置的实验表明，与现有样式转移方法相比，我们的量化框架的视觉保真度明显更高。
The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However, this type of approach cannot guarantee visual fidelity, i.e., the generated artworks should be indistinguishable from real ones. In this paper, we devise a new style transfer framework called QuantArt for high visual-fidelity stylization. QuantArt pushes the latent representation of the generated artwork toward the centroids of the real artwork distribution with vector quantization. By fusing the quantized and continuous latent representations, QuantArt allows flexible control over the generated artworks in terms of content preservation, style similarity, and visual fidelity. Experiments on various style transfer settings show that our QuantArt framework achieves significantly higher visual fidelity compared with the existing style transfer methods.
