论文标题
字体生成缺失印象标签
Font Generation with Missing Impression Labels
论文作者
论文摘要
我们的目标是通过训练具有带有印象标签的字体数据集的生成对抗网络来生成具有特定印象的字体。主要困难是字体印象是模棱两可的，没有印象标签并不总是意味着字体没有印象。本文提出了一种字体生成模型，可抵抗缺失印象标签。提出的方法的关键思想是（1）基于共发生的缺失标签估计器和（2）印象标签空间压缩机。首先是根据数据集中标签的同时出现插值缺少印象标签，并将其用于训练模型作为已完成的标签条件。第二个是一个编码器模块，可将高维的印象空间压缩为低维空间。我们证明了所提出的模型使用具有定性和定量评估的多标签数据生成高质量的字体图像。
Our goal is to generate fonts with specific impressions, by training a generative adversarial network with a font dataset with impression labels. The main difficulty is that font impression is ambiguous and the absence of an impression label does not always mean that the font does not have the impression. This paper proposes a font generation model that is robust against missing impression labels. The key ideas of the proposed method are (1)a co-occurrence-based missing label estimator and (2)an impression label space compressor. The first is to interpolate missing impression labels based on the co-occurrence of labels in the dataset and use them for training the model as completed label conditions. The second is an encoder-decoder module to compress the high-dimensional impression space into low-dimensional. We proved that the proposed model generates high-quality font images using multi-label data with missing labels through qualitative and quantitative evaluations.
