@inproceedings{srivatsan-etal-2019-deep,
title = "A Deep Factorization of Style and Structure in Fonts",
author = "Srivatsan, Nikita and
Barron, Jonathan and
Klein, Dan and
Berg-Kirkpatrick, Taylor",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1225",
doi = "10.18653/v1/D19-1225",
pages = "2195--2205",
abstract = "We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself. When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.",
}
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<abstract>We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself. When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.</abstract>
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%0 Conference Proceedings
%T A Deep Factorization of Style and Structure in Fonts
%A Srivatsan, Nikita
%A Barron, Jonathan
%A Klein, Dan
%A Berg-Kirkpatrick, Taylor
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F srivatsan-etal-2019-deep
%X We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself. When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.
%R 10.18653/v1/D19-1225
%U https://aclanthology.org/D19-1225
%U https://doi.org/10.18653/v1/D19-1225
%P 2195-2205
Markdown (Informal)
[A Deep Factorization of Style and Structure in Fonts](https://aclanthology.org/D19-1225) (Srivatsan et al., EMNLP-IJCNLP 2019)
ACL
- Nikita Srivatsan, Jonathan Barron, Dan Klein, and Taylor Berg-Kirkpatrick. 2019. A Deep Factorization of Style and Structure in Fonts. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2195–2205, Hong Kong, China. Association for Computational Linguistics.