# Learning Perceptual Manifold of Fonts

arXiv: https://arxiv.org/abs/2106.09198 · HTML: https://mlsanity.com/paper/2106.09198/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [DeepFont: Identify Your Font from An Image](https://mlsanity.com/paper/1507.03196/) (2015)
- [From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators](https://mlsanity.com/paper/1603.02003/) (2016)

## Abstract (arXiv)

Along the rapid development of deep learning techniques in generative models, it is becoming an urgent issue to combine machine intelligence with human intelligence to solve the practical applications. Motivated by this methodology, this work aims to adjust the machine generated character fonts with the effort of human workers in the perception study. Although numerous fonts are available online for public usage, it is difficult and challenging to generate and explore a font to meet the preferences for common users. To solve the specific issue, we propose the perceptual manifold of fonts to visualize the perceptual adjustment in the latent space of a generative model of fonts. In our framework, we adopt the variational autoencoder network for the font generation. Then, we conduct a perceptual study on the generated fonts from the multi-dimensional latent space of the generative model. After we obtained the distribution data of specific preferences, we utilize manifold learning approach to visualize the font distribution. In contrast to the conventional user interface in our user study, the proposed font-exploring user interface is efficient and helpful in the designated user preference.