论文标题
TSIT：图像到图像翻译的简单且通用的框架
TSIT: A Simple and Versatile Framework for Image-to-Image Translation
论文作者
论文摘要
我们引入了一个简单且通用的框架，用于图像到图像翻译。我们发掘了标准化层的重要性，并提供了一个精心设计的两流生成模型，并以粗到精细的方式具有新提出的特征转换。这允许网络有效地捕获和融合多尺度的语义结构信息和样式表示形式，从而允许我们的方法扩展到无监督和监督设置中的各种任务。不需要其他约束（例如，循环一致性），这有助于一种非常简单的方法。具有任意样式控制的多模式图像合成。一项系统的研究将所提出的方法与几种最新任务特定的基准进行了比较，从而验证了其在感知质量和定量评估中的有效性。
We introduce a simple and versatile framework for image-to-image translation. We unearth the importance of normalization layers, and provide a carefully designed two-stream generative model with newly proposed feature transformations in a coarse-to-fine fashion. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network, permitting our method to scale to various tasks in both unsupervised and supervised settings. No additional constraints (e.g., cycle consistency) are needed, contributing to a very clean and simple method. Multi-modal image synthesis with arbitrary style control is made possible. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.
