字体的共享潜空间及其噪声干扰
Shared Latent Space of Font Shapes and Their Noisy Impressions
摘要：
字体或字体的风格通常与特定的印象有关，例如沉重，现代或优雅。这表明字体形状与其印象之间存在一定的相关性。为了理解这些相关性，本文实现了一个共享的潜在空间，其中字体及其印象被嵌入附近。困难在于，附加到字体的印象词通常非常嘈杂。这是因为印象词是非常主观和多样的。更重要的是，一些印象词与字体形状没有直接的关联，会干扰共享潜空间的实现。因此，我们使用DeepSets来自动增强形状相关词并抑制形状无关词，同时训练共享潜在空间。大规模字体-印象数据集的定量和定性实验结果表明，所提出的方法的共享潜在空间描述的相关性，特别是形状相关的印象词。
英文摘要：
Styles of typefaces or fonts are often associated with specific impressions, such as heavy, contemporary, or elegant. This indicates that there are certain correlations between font shapes and their impressions. To understand the correlations, this paper realizes a shared latent space where a font and its impressions are embedded nearby. The difficulty is that the impression words attached to a font are often very noisy. This is because impression words are very subjective and diverse. More importantly, some impression words have no direct relevance to the font shapes and will disturb the realization of the shared latent space. We, therefore, use DeepSets for enhancing shape-relevant words and suppressing shape irrelevant words automatically while training the shared latent space. Quantitative and qualitative experimental results with a large-scale font-impression dataset demonstrate that the shared latent space by the proposed method describes the correlation appropriately, especially for the shape-relevant impression words.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
