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In this paper, we present Neural Glyph, a system that generates novel glyphwithout any training data. The generator and the classifier are trained tocommunicate via visual symbols as a medium, which enforces the generator tocome up with a set of distinctive symbols. Our method results in glyphs thatresemble the human-made glyphs, which may imply that the visual appearances ofexisting glyphs can be attributed to constraints of communication via writing.Important tricks that enable this framework are described and the code is madeavailable.

Generating Novel Glyph without Human Data by Learning to Communicate