  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.
