论文标题
煤气隔离：很少射击的跨语性字体生成器
GAS-NeXt: Few-Shot Cross-Lingual Font Generator
论文作者
论文摘要
生成新字体是一项耗时且劳动密集型的任务，尤其是在具有大量字符中的中文字符的语言中。各种深度学习模型已经证明了具有该样式的一些参考字符有效生成新字体的能力，但是很少有模型支持跨语言字体的生成。本文介绍了Gas-Next，这是一种基于Agis-Net和Font Translator GAN的新型跨语义字体生成器，并改善了诸如Fréchet成立距离（FID）之类的性能指标，结构相似性索引指标（SSIM）和Pixel级级准确度（PIX-ACC）。我们的方法包括替换原始编码器和解码器，并利用AGIS-NET的形状，纹理和局部歧视者，从字体翻译器GAN引起的思想和上下文感知的关注。在我们对英语字体翻译的实验中，与从字体翻译器GAN获得的结果相比，与常规中国字体相比，我们观察到具有不同局部特征的字体的结果。我们还验证了多种语言和数据集的方法。
Generating new fonts is a time-consuming and labor-intensive task, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style, but few models support cross-lingual font generation. This paper presents GAS-NeXt, a novel few-shot cross-lingual font generator based on AGIS-Net and Font Translator GAN, and improve the performance metrics such as Fréchet Inception Distance (FID), Structural Similarity Index Measure(SSIM), and Pixel-level Accuracy (pix-acc). Our approaches include replacing the original encoder and decoder with the idea of layer attention and context-aware attention from Font Translator GAN, while utilizing the shape, texture, and local discriminators of AGIS-Net. In our experiment on English-to-Chinese font translation, we observed better results in fonts with distinct local features than conventional Chinese fonts compared to results obtained from Font Translator GAN. We also validate our method on multiple languages and datasets.
