论文标题
多模式图像样式转移的深度特征旋转
Deep Feature Rotation for Multimodal Image Style Transfer
论文作者
论文摘要
最近，样式转移是一个吸引大量关注的研究领域，它将图像的样式转移到内容目标上。关于样式转移的广泛研究旨在加快处理或产生高质量的风格化图像。大多数方法仅产生内容和样式图像对的输出，而其他一些方法则使用复杂的体系结构，并且只能产生一定数量的输出。在本文中，我们提出了一种简单的方法，用于以许多方式表示样式特征，称为“深度旋转”（DFR），而不仅与更复杂的方法相比，不仅产生了多样化的输出，而且仍能实现有效的风格化。我们的方法代表了嵌入中间功能的多种增强方式，而不会消耗过多的计算费用。我们还通过可视化不同旋转权重的输出来分析我们的方法。我们的代码可在https://github.com/sonnguyen129/deep-feature-rotation上找到。
Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. Our code is available at https://github.com/sonnguyen129/deep-feature-rotation.
