  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.
